1 DATA

1.1 Load data

## Longitudinal data
data_sum <- loadlongitudinaldata(dataset = "DATA_Adults_G1G29.csv", rm_generation1 = 1,rm_generation2 = 7,rm_generation3 = 29)


## Phenotyping steps
data_G0 <- loadfitnessdata(dataset = "Selection_Phenotypage_G0_G7_G8.csv", generation = "G1")
data_G7 <- loadfitnessdata(dataset = "Selection_Phenotypage_G0_G7_G8.csv", generation = "G7")
data_G29 <- loadfitnessdata(dataset = 
                            "PERFORMANCE_Comptage_adultes_G13G14G15G16G17G18G19G20G21G22G23G24G25G26G27G28G29.csv",
                            generation = "29")
head(data_sum)
##   Line Fruit_s Generation         Phase  N Nb_adults       sd    fitness
## 1  CE1  Cherry          2 first_prepool 20   9.15000 6.123939 -0.7819784
## 2  CE1  Cherry          3 first_prepool 10  15.30000 7.631077 -0.2678794
## 3  CE1  Cherry          4 first_prepool  8  15.00000 7.782765 -0.2876821
## 4  CE1  Cherry          5 first_prepool  6  14.50000 6.284903 -0.3215836
## 5  CE2  Cherry          2 first_prepool 20   7.75000 7.758696 -0.9480394
## 6  CE2  Cherry          3 first_prepool  7  14.42857 8.303757 -0.3265219
##   se_fitness
## 1  0.1496562
## 2  0.1577228
## 3  0.1834415
## 4  0.1769518
## 5  0.2238577
## 6  0.2175215
head(data_G0)
##     Treatment Line Fruit_s Nb_eggs Nb_adults SA Emergence_rate
## 993    Cherry  CE1      GF      76         6  0     0.07894737
## 994    Cherry  CE1      GF      89        17  0     0.19101124
## 995    Cherry  CE1      GF      57        12  0     0.21052632
## 996    Cherry  CE1      GF     172        24  0     0.13953488
## 997    Cherry  CE1      GF     173        33  0     0.19075145
## 998    Cherry  CE1      GF      91        18  0     0.19780220
head(data_G7)
##    Treatment Line    Fruit_s Nb_eggs Nb_adults SA Emergence_rate
## 3 Strawberry  CR4  Cranberry     152        68  0      0.4473684
## 4  Cranberry  CR4  Cranberry     246        25  1      0.1016260
## 5     Cherry  CR4  Cranberry     238        29  0      0.1218487
## 6     Cherry  CR4  Cranberry     166        23  0      0.1385542
## 8  Cranberry  FR3 Strawberry     204         5  0      0.0245098
## 9 Strawberry  FR3 Strawberry     124        45  1      0.3629032
head(data_G29)
##       Treatment Line Fruit_s Nb_eggs Nb_adults SA Emergence_rate
## 5392 Strawberry  CEA  Cherry     196        16  0     0.08163265
## 5393 Strawberry  CEA  Cherry     192        30  0     0.15625000
## 5394 Strawberry  CEA  Cherry     160        17  0     0.10625000
## 5395 Strawberry  CEA  Cherry     106         9  0     0.08490566
## 5396 Strawberry  CEA  Cherry     119        14  0     0.11764706
## 5397 Strawberry  CEA  Cherry     204        24  0     0.11764706
dim(data_G29)
## [1] 990   7
## Add line variable
levels(data_G0$Line) <- rep("Anc", nlevels(data_G0$Line))

## Combine datsets
data <- rbind(data_G0, data_G7, data_G29) 
data <- data.frame(Generation = c(rep("0", nrow(data_G0)), rep("7", nrow(data_G7)), rep("29", nrow(data_G29))), data, Obs= as.factor(1:nrow(data)))
head(data)
##     Generation Treatment Line Fruit_s Nb_eggs Nb_adults SA Emergence_rate Obs
## 993          0    Cherry  Anc      GF      76         6  0     0.07894737   1
## 994          0    Cherry  Anc      GF      89        17  0     0.19101124   2
## 995          0    Cherry  Anc      GF      57        12  0     0.21052632   3
## 996          0    Cherry  Anc      GF     172        24  0     0.13953488   4
## 997          0    Cherry  Anc      GF     173        33  0     0.19075145   5
## 998          0    Cherry  Anc      GF      91        18  0     0.19780220   6
## New variable for analyses
data$Generation_Fruit_s_Treatment <- as.factor(paste(data$Generation, data$Fruit_s, data$Treatment, sep="_"))
data$Line_Treatment <- as.factor(paste(data$Line, data$Treatment, sep="_"))
data$Treatmentrel <- relevel(data$Treatment, ref="Strawberry")

1.2 Compute fitess change per population

mfitness <- MASS::glm.nb(Nb_adults ~ Treatment, data=data)
summary(mfitness)
## 
## Call:
## MASS::glm.nb(formula = Nb_adults ~ Treatment, data = data, init.theta = 1.909520807, 
##     link = log)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.2900  -0.8197  -0.1146   0.4139   2.7450  
## 
## Coefficients:
##                     Estimate Std. Error z value Pr(>|z|)    
## (Intercept)          3.39580    0.03163 107.365   <2e-16 ***
## TreatmentCranberry  -0.05073    0.04470  -1.135    0.256    
## TreatmentStrawberry  0.02466    0.04471   0.552    0.581    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Negative Binomial(1.9095) family taken to be 1)
## 
##     Null deviance: 1892.5  on 1673  degrees of freedom
## Residual deviance: 1889.5  on 1671  degrees of freedom
## AIC: 14468
## 
## Number of Fisher Scoring iterations: 1
## 
## 
##               Theta:  1.9095 
##           Std. Err.:  0.0698 
## 
##  2 x log-likelihood:  -14460.0940
pr1 <- profile(mfitness, alpha = 0.1)

MASS:::plot.profile(pr1)

### Fitness
mfitness <- MASS::glm.nb(Nb_adults ~ -1 + Treatment + Line:Treatment, data=data)
summary(mfitness)
## 
## Call:
## MASS::glm.nb(formula = Nb_adults ~ -1 + Treatment + Line:Treatment, 
##     data = data, init.theta = 2.366102685, link = log)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.8419  -0.7673  -0.0402   0.4835   3.7740  
## 
## Coefficients:
##                             Estimate Std. Error z value Pr(>|z|)    
## TreatmentCherry              3.21848    0.06802  47.318  < 2e-16 ***
## TreatmentCranberry           3.26385    0.06789  48.077  < 2e-16 ***
## TreatmentStrawberry          3.35446    0.06764  49.590  < 2e-16 ***
## TreatmentCherry:LineCE1     -0.27404    0.22837  -1.200 0.230153    
## TreatmentCranberry:LineCE1  -0.91247    0.26333  -3.465 0.000530 ***
## TreatmentStrawberry:LineCE1  0.09553    0.24774   0.386 0.699777    
## TreatmentCherry:LineCE2     -0.56172    0.35753  -1.571 0.116160    
## TreatmentCranberry:LineCE2  -0.20358    0.40139  -0.507 0.612024    
## TreatmentStrawberry:LineCE2  0.22906    0.47936   0.478 0.632752    
## TreatmentCherry:LineCE3      0.18528    0.19923   0.930 0.352387    
## TreatmentCranberry:LineCE3  -0.11040    0.20830  -0.530 0.596097    
## TreatmentStrawberry:LineCE3 -0.01404    0.19953  -0.070 0.943891    
## TreatmentCherry:LineCE4      0.12791    0.31015   0.412 0.680033    
## TreatmentCranberry:LineCE4   0.46905    0.30646   1.531 0.125891    
## TreatmentStrawberry:LineCE4  0.58713    0.33925   1.731 0.083510 .  
## TreatmentCherry:LineCR1      0.63167    0.66973   0.943 0.345592    
## TreatmentCranberry:LineCR1   0.26984    0.34288   0.787 0.431298    
## TreatmentStrawberry:LineCR1 -0.46408    0.49363  -0.940 0.347144    
## TreatmentCherry:LineCR2      0.37884    0.34225   1.107 0.268337    
## TreatmentCranberry:LineCR2  -0.10685    0.28660  -0.373 0.709287    
## TreatmentStrawberry:LineCR2  0.23598    0.34224   0.690 0.490496    
## TreatmentCherry:LineCR3     -0.62165    0.17548  -3.543 0.000396 ***
## TreatmentCranberry:LineCR3   0.05810    0.16264   0.357 0.720928    
## TreatmentStrawberry:LineCR3  0.10658    0.16516   0.645 0.518711    
## TreatmentCherry:LineCR4      0.20904    0.30927   0.676 0.499096    
## TreatmentCranberry:LineCR4   0.35156    0.28201   1.247 0.212540    
## TreatmentStrawberry:LineCR4  0.52917    0.30532   1.733 0.083064 .  
## TreatmentCherry:LineCR5      0.53338    0.30635   1.741 0.081667 .  
## TreatmentCranberry:LineCR5   0.18840    0.26365   0.715 0.474855    
## TreatmentStrawberry:LineCR5  0.10344    0.34367   0.301 0.763430    
## TreatmentCherry:LineFR1      0.34257    0.14022   2.443 0.014561 *  
## TreatmentCranberry:LineFR1   0.35172    0.14181   2.480 0.013131 *  
## TreatmentStrawberry:LineFR1  0.33051    0.13626   2.426 0.015285 *  
## TreatmentCherry:LineFR2      0.35574    0.28238   1.260 0.207744    
## TreatmentCranberry:LineFR2  -0.16526    0.28734  -0.575 0.565206    
## TreatmentStrawberry:LineFR2  0.09023    0.28343   0.318 0.750229    
## TreatmentCherry:LineFR3      0.57926    0.30600   1.893 0.058361 .  
## TreatmentCranberry:LineFR3   0.17014    0.30917   0.550 0.582113    
## TreatmentStrawberry:LineFR3  0.13336    0.26329   0.507 0.612498    
## TreatmentCherry:LineFR4      0.72973    0.16699   4.370 1.24e-05 ***
## TreatmentCranberry:LineFR4   0.36284    0.17617   2.060 0.039432 *  
## TreatmentStrawberry:LineFR4  0.58063    0.17071   3.401 0.000671 ***
## TreatmentCherry:LineFR5      0.30788    0.67578   0.456 0.648677    
## TreatmentCranberry:LineFR5   0.39971    0.67297   0.594 0.552542    
## TreatmentStrawberry:LineFR5  0.18650    0.47999   0.389 0.697600    
## TreatmentCherry:LineCEA      0.14767    0.14094   1.048 0.294766    
## TreatmentCranberry:LineCEA  -0.21774    0.14241  -1.529 0.126279    
## TreatmentStrawberry:LineCEA  0.01971    0.14073   0.140 0.888596    
## TreatmentCherry:LineCEB      0.25038    0.14054   1.781 0.074831 .  
## TreatmentCranberry:LineCEB  -0.59200    0.14492  -4.085 4.41e-05 ***
## TreatmentStrawberry:LineCEB -0.15851    0.14152  -1.120 0.262699    
## TreatmentCherry:LineCEC      0.35854    0.14017   2.558 0.010528 *  
## TreatmentCranberry:LineCEC  -0.20671    0.14235  -1.452 0.146475    
## TreatmentStrawberry:LineCEC  0.21131    0.14002   1.509 0.131258    
## TreatmentCherry:LineCRA      0.32538    0.14028   2.320 0.020366 *  
## TreatmentCranberry:LineCRA  -0.14885    0.14204  -1.048 0.294660    
## TreatmentStrawberry:LineCRA -0.29731    0.14224  -2.090 0.036591 *  
## TreatmentCherry:LineCRB      0.16930    0.14086   1.202 0.229393    
## TreatmentCranberry:LineCRB   0.29720    0.14016   2.120 0.033966 *  
## TreatmentStrawberry:LineCRB -0.24539    0.14196  -1.729 0.083870 .  
## TreatmentCherry:LineCRC      0.08474    0.14121   0.600 0.548430    
## TreatmentCranberry:LineCRC   0.02704    0.14120   0.191 0.848148    
## TreatmentStrawberry:LineCRC -0.28951    0.14219  -2.036 0.041748 *  
## TreatmentCherry:LineCRD      0.54736    0.13959   3.921 8.81e-05 ***
## TreatmentCranberry:LineCRD  -0.15628    0.14208  -1.100 0.271367    
## TreatmentStrawberry:LineCRD -1.02555    0.14802  -6.928 4.26e-12 ***
## TreatmentCherry:LineCRE     -0.10496    0.14211  -0.739 0.460165    
## TreatmentCranberry:LineCRE   0.72513    0.13897   5.218 1.81e-07 ***
## TreatmentStrawberry:LineCRE -0.16810    0.14157  -1.187 0.235050    
## TreatmentCherry:LineFRA     -0.50599    0.14466  -3.498 0.000469 ***
## TreatmentCranberry:LineFRA  -0.25159    0.14260  -1.764 0.077692 .  
## TreatmentStrawberry:LineFRA  0.15709    0.14021   1.120 0.262542    
## TreatmentCherry:LineFRB      0.19377    0.14076   1.377 0.168632    
## TreatmentCranberry:LineFRB   0.04304    0.14113   0.305 0.760404    
## TreatmentStrawberry:LineFRB  0.56023    0.13903   4.030 5.59e-05 ***
## TreatmentCherry:LineFRC      0.32152    0.14029   2.292 0.021918 *  
## TreatmentCranberry:LineFRC   0.52110    0.13948   3.736 0.000187 ***
## TreatmentStrawberry:LineFRC  0.36641    0.13954   2.626 0.008643 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Negative Binomial(2.3661) family taken to be 1)
## 
##     Null deviance: 97071.5  on 1674  degrees of freedom
## Residual deviance:  1897.6  on 1596  degrees of freedom
## AIC: 14287
## 
## Number of Fisher Scoring iterations: 1
## 
## 
##               Theta:  2.3661 
##           Std. Err.:  0.0906 
## 
##  2 x log-likelihood:  -14129.0600
CIfitness <- confint(mfitness)

pr1 <- profile(mfitness, alpha = 0.1)

# pdf(file="figures/Profile.pdf")
# MASS:::plot.profile(pr1)
# dev.off()



### 
### Fecundity
### 
mfecundity <- MASS::glm.nb(Nb_eggs ~ -1+Treatment+Line:Treatment, data=data)
summary(mfecundity)
## 
## Call:
## MASS::glm.nb(formula = Nb_eggs ~ -1 + Treatment + Line:Treatment, 
##     data = data, init.theta = 8.2113987, link = log)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -5.7949  -0.6864  -0.0232   0.5593   3.0051  
## 
## Coefficients:
##                             Estimate Std. Error z value Pr(>|z|)    
## TreatmentCherry              5.00723    0.03584 139.699  < 2e-16 ***
## TreatmentCranberry           4.81096    0.03604 133.472  < 2e-16 ***
## TreatmentStrawberry          4.68065    0.03620 129.294  < 2e-16 ***
## TreatmentCherry:LineCE1      0.21744    0.11833   1.838 0.066115 .  
## TreatmentCranberry:LineCE1   0.39304    0.13118   2.996 0.002734 ** 
## TreatmentStrawberry:LineCE1  0.48342    0.13133   3.681 0.000232 ***
## TreatmentCherry:LineCE2      0.55345    0.18081   3.061 0.002206 ** 
## TreatmentCranberry:LineCE2   0.43255    0.20893   2.070 0.038430 *  
## TreatmentStrawberry:LineCE2  0.27518    0.25636   1.073 0.283097    
## TreatmentCherry:LineCE3      0.18914    0.10525   1.797 0.072338 .  
## TreatmentCranberry:LineCE3   0.33605    0.10924   3.076 0.002096 ** 
## TreatmentStrawberry:LineCE3  0.43781    0.10554   4.148 3.35e-05 ***
## TreatmentCherry:LineCE4      0.29906    0.16320   1.832 0.066879 .  
## TreatmentCranberry:LineCE4   0.27910    0.16397   1.702 0.088737 .  
## TreatmentStrawberry:LineCE4  0.69925    0.18141   3.855 0.000116 ***
## TreatmentCherry:LineCR1     -0.27103    0.36310  -0.746 0.455399    
## TreatmentCranberry:LineCR1   0.07562    0.18339   0.412 0.680090    
## TreatmentStrawberry:LineCR1  0.23568    0.25664   0.918 0.358456    
## TreatmentCherry:LineCR2      0.17033    0.18205   0.936 0.349452    
## TreatmentCranberry:LineCR2   0.24740    0.15052   1.644 0.100242    
## TreatmentStrawberry:LineCR2  0.19455    0.18348   1.060 0.288993    
## TreatmentCherry:LineCR3      0.28872    0.08921   3.236 0.001210 ** 
## TreatmentCranberry:LineCR3   0.44703    0.08571   5.216 1.83e-07 ***
## TreatmentStrawberry:LineCR3  0.34126    0.08792   3.882 0.000104 ***
## TreatmentCherry:LineCR4      0.16779    0.16362   1.025 0.305142    
## TreatmentCranberry:LineCR4   0.48150    0.14978   3.215 0.001306 ** 
## TreatmentStrawberry:LineCR4  0.32330    0.16434   1.967 0.049161 *  
## TreatmentCherry:LineCR5      0.08652    0.16391   0.528 0.597611    
## TreatmentCranberry:LineCR5   0.38990    0.13959   2.793 0.005218 ** 
## TreatmentStrawberry:LineCR5  0.25562    0.18317   1.396 0.162852    
## TreatmentCherry:LineFR1      0.28390    0.07424   3.824 0.000131 ***
## TreatmentCranberry:LineFR1   0.37275    0.07544   4.941 7.78e-07 ***
## TreatmentStrawberry:LineFR1  0.51854    0.07272   7.130 1.00e-12 ***
## TreatmentCherry:LineFR2      0.05431    0.15046   0.361 0.718126    
## TreatmentCranberry:LineFR2   0.52657    0.14966   3.519 0.000434 ***
## TreatmentStrawberry:LineFR2  0.27283    0.15094   1.807 0.070687 .  
## TreatmentCherry:LineFR3      0.05916    0.16402   0.361 0.718350    
## TreatmentCranberry:LineFR3   0.31419    0.16384   1.918 0.055159 .  
## TreatmentStrawberry:LineFR3  0.30784    0.14029   2.194 0.028215 *  
## TreatmentCherry:LineFR4      0.15635    0.08942   1.749 0.080364 .  
## TreatmentCranberry:LineFR4   0.56758    0.09346   6.073 1.25e-09 ***
## TreatmentStrawberry:LineFR4  0.50984    0.09157   5.568 2.58e-08 ***
## TreatmentCherry:LineFR5     -0.04438    0.36064  -0.123 0.902049    
## TreatmentCranberry:LineFR5   0.93843    0.35534   2.641 0.008268 ** 
## TreatmentStrawberry:LineFR5  0.67594    0.25409   2.660 0.007808 ** 
## TreatmentCherry:LineCEA     -0.33378    0.07520  -4.438 9.06e-06 ***
## TreatmentCranberry:LineCEA   0.05785    0.07493   0.772 0.440078    
## TreatmentStrawberry:LineCEA  0.25933    0.07489   3.463 0.000535 ***
## TreatmentCherry:LineCEB     -0.38291    0.07531  -5.085 3.68e-07 ***
## TreatmentCranberry:LineCEB  -0.08653    0.07520  -1.151 0.249844    
## TreatmentStrawberry:LineCEB  0.14071    0.07509   1.874 0.060950 .  
## TreatmentCherry:LineCEC     -0.37932    0.07530  -5.038 4.72e-07 ***
## TreatmentCranberry:LineCEC   0.06855    0.07491   0.915 0.360163    
## TreatmentStrawberry:LineCEC  0.18483    0.07501   2.464 0.013739 *  
## TreatmentCherry:LineCRA     -0.31070    0.07516  -4.134 3.56e-05 ***
## TreatmentCranberry:LineCRA  -0.15067    0.07533  -2.000 0.045469 *  
## TreatmentStrawberry:LineCRA  0.16825    0.07504   2.242 0.024953 *  
## TreatmentCherry:LineCRB     -0.37347    0.07529  -4.961 7.02e-07 ***
## TreatmentCranberry:LineCRB   0.10950    0.07485   1.463 0.143450    
## TreatmentStrawberry:LineCRB  0.19988    0.07499   2.666 0.007687 ** 
## TreatmentCherry:LineCRC     -0.24420    0.07503  -3.255 0.001134 ** 
## TreatmentCranberry:LineCRC  -0.07155    0.07517  -0.952 0.341128    
## TreatmentStrawberry:LineCRC  0.18174    0.07502   2.423 0.015406 *  
## TreatmentCherry:LineCRD     -0.26201    0.07506  -3.491 0.000482 ***
## TreatmentCranberry:LineCRD   0.10853    0.07485   1.450 0.147050    
## TreatmentStrawberry:LineCRD -0.03786    0.07544  -0.502 0.615747    
## TreatmentCherry:LineCRE     -0.33783    0.07521  -4.492 7.06e-06 ***
## TreatmentCranberry:LineCRE   0.01307    0.07501   0.174 0.861633    
## TreatmentStrawberry:LineCRE  0.18226    0.07502   2.430 0.015116 *  
## TreatmentCherry:LineFRA     -0.34947    0.07524  -4.645 3.40e-06 ***
## TreatmentCranberry:LineFRA  -0.06923    0.07516  -0.921 0.357045    
## TreatmentStrawberry:LineFRA  0.12446    0.07512   1.657 0.097540 .  
## TreatmentCherry:LineFRB     -0.49820    0.07557  -6.592 4.33e-11 ***
## TreatmentCranberry:LineFRB  -0.20679    0.07545  -2.741 0.006127 ** 
## TreatmentStrawberry:LineFRB  0.14232    0.07509   1.895 0.058043 .  
## TreatmentCherry:LineFRC     -0.58678    0.07580  -7.742 9.82e-15 ***
## TreatmentCranberry:LineFRC  -0.10565    0.07523  -1.404 0.160247    
## TreatmentStrawberry:LineFRC  0.21520    0.07496   2.871 0.004094 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Negative Binomial(8.2114) family taken to be 1)
## 
##     Null deviance: 889732.5  on 1674  degrees of freedom
## Residual deviance:   1730.1  on 1596  degrees of freedom
## AIC: 17660
## 
## Number of Fisher Scoring iterations: 1
## 
## 
##               Theta:  8.211 
##           Std. Err.:  0.301 
## 
##  2 x log-likelihood:  -17501.650
CIfecundity <- confint(mfecundity)

### 
### Egg-to-adult viability
### 
## 4 tubes with more adults than eggs
sum(data$Nb_adults>data$Nb_eggs)
## [1] 4
## Number of adults=number of eggs
data$Nb_eggs[data$Nb_adults>data$Nb_eggs] <- data$Nb_adults[data$Nb_adults>data$Nb_eggs]

## Fit model
megg_to_ad <- glm(cbind(Nb_adults, Nb_eggs) ~ -1+Treatment+Line:Treatment, data=data, family="binomial")
summary(megg_to_ad)
## 
## Call:
## glm(formula = cbind(Nb_adults, Nb_eggs) ~ -1 + Treatment + Line:Treatment, 
##     family = "binomial", data = data)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -11.2550   -2.2309    0.0851    2.0910   13.2204  
## 
## Coefficients:
##                              Estimate Std. Error z value Pr(>|z|)    
## TreatmentCherry             -1.788754   0.021611 -82.769  < 2e-16 ***
## TreatmentCranberry          -1.547115   0.021536 -71.838  < 2e-16 ***
## TreatmentStrawberry         -1.326194   0.021024 -63.080  < 2e-16 ***
## TreatmentCherry:LineCE1     -0.494166   0.079164  -6.242 4.31e-10 ***
## TreatmentCranberry:LineCE1  -1.305517   0.114258 -11.426  < 2e-16 ***
## TreatmentStrawberry:LineCE1 -0.387890   0.071590  -5.418 6.02e-08 ***
## TreatmentCherry:LineCE2     -1.115171   0.137737  -8.096 5.66e-16 ***
## TreatmentCranberry:LineCE2  -0.636123   0.133602  -4.761 1.92e-06 ***
## TreatmentStrawberry:LineCE2 -0.046115   0.133611  -0.345 0.729990    
## TreatmentCherry:LineCE3     -0.003858   0.058741  -0.066 0.947636    
## TreatmentCranberry:LineCE3  -0.446447   0.067136  -6.650 2.93e-11 ***
## TreatmentStrawberry:LineCE3 -0.451848   0.060226  -7.503 6.26e-14 ***
## TreatmentCherry:LineCE4     -0.171143   0.092203  -1.856 0.063432 .  
## TreatmentCranberry:LineCE4   0.189949   0.080499   2.360 0.018292 *  
## TreatmentStrawberry:LineCE4 -0.112122   0.080302  -1.396 0.162639    
## TreatmentCherry:LineCR1      0.902703   0.174687   5.168 2.37e-07 ***
## TreatmentCranberry:LineCR1   0.194219   0.098234   1.977 0.048029 *  
## TreatmentStrawberry:LineCR1 -0.699759   0.178558  -3.919 8.89e-05 ***
## TreatmentCherry:LineCR2      0.208505   0.093417   2.232 0.025617 *  
## TreatmentCranberry:LineCR2  -0.354251   0.092819  -3.817 0.000135 ***
## TreatmentStrawberry:LineCR2  0.041436   0.096161   0.431 0.666543    
## TreatmentCherry:LineCR3     -0.910368   0.068209 -13.347  < 2e-16 ***
## TreatmentCranberry:LineCR3  -0.388928   0.049294  -7.890 3.02e-15 ***
## TreatmentStrawberry:LineCR3 -0.234674   0.048389  -4.850 1.24e-06 ***
## TreatmentCherry:LineCR4      0.041249   0.089954   0.459 0.646551    
## TreatmentCranberry:LineCR4  -0.129940   0.076068  -1.708 0.087598 .  
## TreatmentStrawberry:LineCR4  0.205871   0.076808   2.680 0.007355 ** 
## TreatmentCherry:LineCR5      0.446858   0.079930   5.591 2.26e-08 ***
## TreatmentCranberry:LineCR5  -0.201495   0.076001  -2.651 0.008020 ** 
## TreatmentStrawberry:LineCR5 -0.152185   0.100555  -1.513 0.130167    
## TreatmentCherry:LineFR1      0.058675   0.039773   1.475 0.140147    
## TreatmentCranberry:LineFR1  -0.021037   0.039810  -0.528 0.597192    
## TreatmentStrawberry:LineFR1 -0.188028   0.037401  -5.027 4.97e-07 ***
## TreatmentCherry:LineFR2      0.301431   0.078714   3.829 0.000128 ***
## TreatmentCranberry:LineFR2  -0.691834   0.093722  -7.382 1.56e-13 ***
## TreatmentStrawberry:LineFR2 -0.197779   0.083186  -2.378 0.017428 *  
## TreatmentCherry:LineFR3      0.520102   0.078819   6.599 4.15e-11 ***
## TreatmentCranberry:LineFR3  -0.144052   0.090025  -1.600 0.109570    
## TreatmentStrawberry:LineFR3 -0.174482   0.076043  -2.295 0.021761 *  
## TreatmentCherry:LineFR4      0.573374   0.042230  13.577  < 2e-16 ***
## TreatmentCranberry:LineFR4  -0.204735   0.047960  -4.269 1.96e-05 ***
## TreatmentStrawberry:LineFR4  0.070797   0.042863   1.652 0.098590 .  
## TreatmentCherry:LineFR5      0.352270   0.192020   1.835 0.066573 .  
## TreatmentCranberry:LineFR5  -0.538716   0.171142  -3.148 0.001645 ** 
## TreatmentStrawberry:LineFR5 -0.489433   0.131504  -3.722 0.000198 ***
## TreatmentCherry:LineCEA      0.481448   0.043922  10.961  < 2e-16 ***
## TreatmentCranberry:LineCEA  -0.275592   0.048007  -5.741 9.43e-09 ***
## TreatmentStrawberry:LineCEA -0.239612   0.042686  -5.613 1.98e-08 ***
## TreatmentCherry:LineCEB      0.633291   0.042808  14.794  < 2e-16 ***
## TreatmentCranberry:LineCEB  -0.505472   0.055352  -9.132  < 2e-16 ***
## TreatmentStrawberry:LineCEB -0.299214   0.045550  -6.569 5.07e-11 ***
## TreatmentCherry:LineCEC      0.737860   0.041531  17.766  < 2e-16 ***
## TreatmentCranberry:LineCEC  -0.275258   0.047797  -5.759 8.47e-09 ***
## TreatmentStrawberry:LineCEC  0.026482   0.040515   0.654 0.513343    
## TreatmentCherry:LineCRA      0.636074   0.041649  15.272  < 2e-16 ***
## TreatmentCranberry:LineCRA   0.001821   0.047524   0.038 0.969433    
## TreatmentStrawberry:LineCRA -0.465566   0.047651  -9.770  < 2e-16 ***
## TreatmentCherry:LineCRB      0.542770   0.043785  12.396  < 2e-16 ***
## TreatmentCranberry:LineCRB   0.187694   0.040669   4.615 3.93e-06 ***
## TreatmentStrawberry:LineCRB -0.445272   0.046725  -9.530  < 2e-16 ***
## TreatmentCherry:LineCRC      0.328942   0.044466   7.398 1.39e-13 ***
## TreatmentCranberry:LineCRC   0.098592   0.044676   2.207 0.027328 *  
## TreatmentStrawberry:LineCRC -0.471252   0.047486  -9.924  < 2e-16 ***
## TreatmentCherry:LineCRD      0.809372   0.039095  20.703  < 2e-16 ***
## TreatmentCranberry:LineCRD  -0.264808   0.046878  -5.649 1.62e-08 ***
## TreatmentStrawberry:LineCRD -0.987691   0.063323 -15.598  < 2e-16 ***
## TreatmentCherry:LineCRE      0.232873   0.047551   4.897 9.72e-07 ***
## TreatmentCranberry:LineCRE   0.712061   0.036727  19.388  < 2e-16 ***
## TreatmentStrawberry:LineCRE -0.350362   0.045574  -7.688 1.50e-14 ***
## TreatmentCherry:LineFRA     -0.156524   0.054733  -2.860 0.004239 ** 
## TreatmentCranberry:LineFRA  -0.182362   0.048928  -3.727 0.000194 ***
## TreatmentStrawberry:LineFRA  0.032626   0.041352   0.789 0.430124    
## TreatmentCherry:LineFRB      0.691975   0.043965  15.739  < 2e-16 ***
## TreatmentCranberry:LineFRB   0.249832   0.044927   5.561 2.69e-08 ***
## TreatmentStrawberry:LineFRB  0.407777   0.037045  11.008  < 2e-16 ***
## TreatmentCherry:LineFRC      0.908301   0.042838  21.203  < 2e-16 ***
## TreatmentCranberry:LineFRC   0.626745   0.039018  16.063  < 2e-16 ***
## TreatmentStrawberry:LineFRC  0.151208   0.038709   3.906 9.37e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 142163  on 1674  degrees of freedom
## Residual deviance:  17859  on 1596  degrees of freedom
## AIC: 25827
## 
## Number of Fisher Scoring iterations: 5
CImegg_to_ad <- confint(megg_to_ad)

####### 
####### Build data_logchange dataset
####### 
## Extract all CIs
## Check that the coef are in the same order
identical(names(coef(mfitness)), names(coef(mfecundity)))
## [1] TRUE
identical(names(coef(mfitness)), names(coef(megg_to_ad)))
## [1] TRUE
data_logchange <- data.frame(logchange=coef(mfitness),
                             lowCIlogfitnesschange=CIfitness[, 1],
                             upCIlogfitnesschange=CIfitness[, 2],
                             logfecundchange=coef(mfecundity),
                             lowCIlogfecundchange=CIfecundity[, 1],
                             upCIlogfecundchange=CIfecundity[, 2],
                             logeggtoadchange=coef(megg_to_ad),
                             lowCIlogeggtoadchange=CImegg_to_ad[, 1],
                             upCIlogeggtoadchange=CImegg_to_ad[, 2])
head(data_logchange)
##                               logchange lowCIlogfitnesschange
## TreatmentCherry              3.21847574             3.0875555
## TreatmentCranberry           3.26384919             3.1331965
## TreatmentStrawberry          3.35445512             3.2243029
## TreatmentCherry:LineCE1     -0.27403677            -0.7021017
## TreatmentCranberry:LineCE1  -0.91247393            -1.4069620
## TreatmentStrawberry:LineCE1  0.09553243            -0.3637574
##                             upCIlogfitnesschange logfecundchange
## TreatmentCherry                        3.3543047       5.0072295
## TreatmentCranberry                     3.3994333       4.8109641
## TreatmentStrawberry                    3.4895811       4.6806486
## TreatmentCherry:LineCE1                0.1969999       0.2174413
## TreatmentCranberry:LineCE1            -0.3690326       0.3930426
## TreatmentStrawberry:LineCE1            0.6127366       0.4834228
##                             lowCIlogfecundchange upCIlogfecundchange
## TreatmentCherry                      4.937707676           5.0782276
## TreatmentCranberry                   4.741037413           4.8823484
## TreatmentStrawberry                  4.610407277           4.7523331
## TreatmentCherry:LineCE1             -0.008054728           0.4563722
## TreatmentCranberry:LineCE1           0.144087552           0.6591387
## TreatmentStrawberry:LineCE1          0.234153348           0.7497917
##                             logeggtoadchange lowCIlogeggtoadchange
## TreatmentCherry                   -1.7887538            -1.8313270
## TreatmentCranberry                -1.5471149            -1.5895200
## TreatmentStrawberry               -1.3261935            -1.3675662
## TreatmentCherry:LineCE1           -0.4941655            -0.6522000
## TreatmentCranberry:LineCE1        -1.3055165            -1.5366802
## TreatmentStrawberry:LineCE1       -0.3878904            -0.5301533
##                             upCIlogeggtoadchange
## TreatmentCherry                       -1.7466080
## TreatmentCranberry                    -1.5050959
## TreatmentStrawberry                   -1.2851499
## TreatmentCherry:LineCE1               -0.3417120
## TreatmentCranberry:LineCE1            -1.0882241
## TreatmentStrawberry:LineCE1           -0.2494137
## Remove estimates from ancestral population
data_logchange <- data_logchange[4:nrow(data_logchange), ]
  
## Extract information regarding line, selective and test media
rowname <- strsplit(rownames(data_logchange), split=":")
Treatment <- as.factor(gsub("Treatment", "", lapply(rowname, `[[`, 1)))
Generation <- Line <- as.factor(gsub("Line", "", lapply(rowname, `[[`, 2)))
## Change Fruit_s levels
Fruit_s <- as.factor(substr(Line, 1, 2))
levels(Fruit_s) <- levels(Treatment)
## Change levels
levels(Generation) <- ifelse(is.na(as.numeric(substr(levels(Generation), 3, 3))), "29", "7")

## Combine data
data_info <- data.frame(Treatment, Line, Fruit_s, Generation)
data_info$Line_Treatement <- paste(data_info$Line, data_info$Treatment, sep="_")
# Add symp and allop
data_info$SA <- as.factor(ifelse(data_info$Treatment == data_info$Fruit_s , 1, 0))
data.frame(names(coef(mfitness))[4:length(coef(mfitness))], data_info)
##    names.coef.mfitness...4.length.coef.mfitness...  Treatment Line    Fruit_s
## 1                          TreatmentCherry:LineCE1     Cherry  CE1     Cherry
## 2                       TreatmentCranberry:LineCE1  Cranberry  CE1     Cherry
## 3                      TreatmentStrawberry:LineCE1 Strawberry  CE1     Cherry
## 4                          TreatmentCherry:LineCE2     Cherry  CE2     Cherry
## 5                       TreatmentCranberry:LineCE2  Cranberry  CE2     Cherry
## 6                      TreatmentStrawberry:LineCE2 Strawberry  CE2     Cherry
## 7                          TreatmentCherry:LineCE3     Cherry  CE3     Cherry
## 8                       TreatmentCranberry:LineCE3  Cranberry  CE3     Cherry
## 9                      TreatmentStrawberry:LineCE3 Strawberry  CE3     Cherry
## 10                         TreatmentCherry:LineCE4     Cherry  CE4     Cherry
## 11                      TreatmentCranberry:LineCE4  Cranberry  CE4     Cherry
## 12                     TreatmentStrawberry:LineCE4 Strawberry  CE4     Cherry
## 13                         TreatmentCherry:LineCR1     Cherry  CR1  Cranberry
## 14                      TreatmentCranberry:LineCR1  Cranberry  CR1  Cranberry
## 15                     TreatmentStrawberry:LineCR1 Strawberry  CR1  Cranberry
## 16                         TreatmentCherry:LineCR2     Cherry  CR2  Cranberry
## 17                      TreatmentCranberry:LineCR2  Cranberry  CR2  Cranberry
## 18                     TreatmentStrawberry:LineCR2 Strawberry  CR2  Cranberry
## 19                         TreatmentCherry:LineCR3     Cherry  CR3  Cranberry
## 20                      TreatmentCranberry:LineCR3  Cranberry  CR3  Cranberry
## 21                     TreatmentStrawberry:LineCR3 Strawberry  CR3  Cranberry
## 22                         TreatmentCherry:LineCR4     Cherry  CR4  Cranberry
## 23                      TreatmentCranberry:LineCR4  Cranberry  CR4  Cranberry
## 24                     TreatmentStrawberry:LineCR4 Strawberry  CR4  Cranberry
## 25                         TreatmentCherry:LineCR5     Cherry  CR5  Cranberry
## 26                      TreatmentCranberry:LineCR5  Cranberry  CR5  Cranberry
## 27                     TreatmentStrawberry:LineCR5 Strawberry  CR5  Cranberry
## 28                         TreatmentCherry:LineFR1     Cherry  FR1 Strawberry
## 29                      TreatmentCranberry:LineFR1  Cranberry  FR1 Strawberry
## 30                     TreatmentStrawberry:LineFR1 Strawberry  FR1 Strawberry
## 31                         TreatmentCherry:LineFR2     Cherry  FR2 Strawberry
## 32                      TreatmentCranberry:LineFR2  Cranberry  FR2 Strawberry
## 33                     TreatmentStrawberry:LineFR2 Strawberry  FR2 Strawberry
## 34                         TreatmentCherry:LineFR3     Cherry  FR3 Strawberry
## 35                      TreatmentCranberry:LineFR3  Cranberry  FR3 Strawberry
## 36                     TreatmentStrawberry:LineFR3 Strawberry  FR3 Strawberry
## 37                         TreatmentCherry:LineFR4     Cherry  FR4 Strawberry
## 38                      TreatmentCranberry:LineFR4  Cranberry  FR4 Strawberry
## 39                     TreatmentStrawberry:LineFR4 Strawberry  FR4 Strawberry
## 40                         TreatmentCherry:LineFR5     Cherry  FR5 Strawberry
## 41                      TreatmentCranberry:LineFR5  Cranberry  FR5 Strawberry
## 42                     TreatmentStrawberry:LineFR5 Strawberry  FR5 Strawberry
## 43                         TreatmentCherry:LineCEA     Cherry  CEA     Cherry
## 44                      TreatmentCranberry:LineCEA  Cranberry  CEA     Cherry
## 45                     TreatmentStrawberry:LineCEA Strawberry  CEA     Cherry
## 46                         TreatmentCherry:LineCEB     Cherry  CEB     Cherry
## 47                      TreatmentCranberry:LineCEB  Cranberry  CEB     Cherry
## 48                     TreatmentStrawberry:LineCEB Strawberry  CEB     Cherry
## 49                         TreatmentCherry:LineCEC     Cherry  CEC     Cherry
## 50                      TreatmentCranberry:LineCEC  Cranberry  CEC     Cherry
## 51                     TreatmentStrawberry:LineCEC Strawberry  CEC     Cherry
## 52                         TreatmentCherry:LineCRA     Cherry  CRA  Cranberry
## 53                      TreatmentCranberry:LineCRA  Cranberry  CRA  Cranberry
## 54                     TreatmentStrawberry:LineCRA Strawberry  CRA  Cranberry
## 55                         TreatmentCherry:LineCRB     Cherry  CRB  Cranberry
## 56                      TreatmentCranberry:LineCRB  Cranberry  CRB  Cranberry
## 57                     TreatmentStrawberry:LineCRB Strawberry  CRB  Cranberry
## 58                         TreatmentCherry:LineCRC     Cherry  CRC  Cranberry
## 59                      TreatmentCranberry:LineCRC  Cranberry  CRC  Cranberry
## 60                     TreatmentStrawberry:LineCRC Strawberry  CRC  Cranberry
## 61                         TreatmentCherry:LineCRD     Cherry  CRD  Cranberry
## 62                      TreatmentCranberry:LineCRD  Cranberry  CRD  Cranberry
## 63                     TreatmentStrawberry:LineCRD Strawberry  CRD  Cranberry
## 64                         TreatmentCherry:LineCRE     Cherry  CRE  Cranberry
## 65                      TreatmentCranberry:LineCRE  Cranberry  CRE  Cranberry
## 66                     TreatmentStrawberry:LineCRE Strawberry  CRE  Cranberry
## 67                         TreatmentCherry:LineFRA     Cherry  FRA Strawberry
## 68                      TreatmentCranberry:LineFRA  Cranberry  FRA Strawberry
## 69                     TreatmentStrawberry:LineFRA Strawberry  FRA Strawberry
## 70                         TreatmentCherry:LineFRB     Cherry  FRB Strawberry
## 71                      TreatmentCranberry:LineFRB  Cranberry  FRB Strawberry
## 72                     TreatmentStrawberry:LineFRB Strawberry  FRB Strawberry
## 73                         TreatmentCherry:LineFRC     Cherry  FRC Strawberry
## 74                      TreatmentCranberry:LineFRC  Cranberry  FRC Strawberry
## 75                     TreatmentStrawberry:LineFRC Strawberry  FRC Strawberry
##    Generation Line_Treatement SA
## 1           7      CE1_Cherry  1
## 2           7   CE1_Cranberry  0
## 3           7  CE1_Strawberry  0
## 4           7      CE2_Cherry  1
## 5           7   CE2_Cranberry  0
## 6           7  CE2_Strawberry  0
## 7           7      CE3_Cherry  1
## 8           7   CE3_Cranberry  0
## 9           7  CE3_Strawberry  0
## 10          7      CE4_Cherry  1
## 11          7   CE4_Cranberry  0
## 12          7  CE4_Strawberry  0
## 13          7      CR1_Cherry  0
## 14          7   CR1_Cranberry  1
## 15          7  CR1_Strawberry  0
## 16          7      CR2_Cherry  0
## 17          7   CR2_Cranberry  1
## 18          7  CR2_Strawberry  0
## 19          7      CR3_Cherry  0
## 20          7   CR3_Cranberry  1
## 21          7  CR3_Strawberry  0
## 22          7      CR4_Cherry  0
## 23          7   CR4_Cranberry  1
## 24          7  CR4_Strawberry  0
## 25          7      CR5_Cherry  0
## 26          7   CR5_Cranberry  1
## 27          7  CR5_Strawberry  0
## 28          7      FR1_Cherry  0
## 29          7   FR1_Cranberry  0
## 30          7  FR1_Strawberry  1
## 31          7      FR2_Cherry  0
## 32          7   FR2_Cranberry  0
## 33          7  FR2_Strawberry  1
## 34          7      FR3_Cherry  0
## 35          7   FR3_Cranberry  0
## 36          7  FR3_Strawberry  1
## 37          7      FR4_Cherry  0
## 38          7   FR4_Cranberry  0
## 39          7  FR4_Strawberry  1
## 40          7      FR5_Cherry  0
## 41          7   FR5_Cranberry  0
## 42          7  FR5_Strawberry  1
## 43         29      CEA_Cherry  1
## 44         29   CEA_Cranberry  0
## 45         29  CEA_Strawberry  0
## 46         29      CEB_Cherry  1
## 47         29   CEB_Cranberry  0
## 48         29  CEB_Strawberry  0
## 49         29      CEC_Cherry  1
## 50         29   CEC_Cranberry  0
## 51         29  CEC_Strawberry  0
## 52         29      CRA_Cherry  0
## 53         29   CRA_Cranberry  1
## 54         29  CRA_Strawberry  0
## 55         29      CRB_Cherry  0
## 56         29   CRB_Cranberry  1
## 57         29  CRB_Strawberry  0
## 58         29      CRC_Cherry  0
## 59         29   CRC_Cranberry  1
## 60         29  CRC_Strawberry  0
## 61         29      CRD_Cherry  0
## 62         29   CRD_Cranberry  1
## 63         29  CRD_Strawberry  0
## 64         29      CRE_Cherry  0
## 65         29   CRE_Cranberry  1
## 66         29  CRE_Strawberry  0
## 67         29      FRA_Cherry  0
## 68         29   FRA_Cranberry  0
## 69         29  FRA_Strawberry  1
## 70         29      FRB_Cherry  0
## 71         29   FRB_Cranberry  0
## 72         29  FRB_Strawberry  1
## 73         29      FRC_Cherry  0
## 74         29   FRC_Cranberry  0
## 75         29  FRC_Strawberry  1
## Compute sample size per line and test medium
sample_size <- aggregate(Nb_eggs~Line:Treatment, length, data=data[data$Generation!="0",])
sample_size$Line_Treatement <- paste(sample_size$Line, sample_size$Treatment, sep="_")
names(sample_size)[3] <- "N"

## Merge the three datasets
data_info <- merge(x=data_info, y=sample_size[, 3:4], by="Line_Treatement")
data_info$Line_Treatment <- paste0("Treatment", data_info$Treatment, ":Line", data_info$Line)
data_logchange$Line_Treatment <- rownames(data_logchange)
data_logchange <- merge(x=data_info, y=data_logchange, by = "Line_Treatment")[, -c(1, 2)]

head(data_logchange)
##   Treatment Line Fruit_s Generation SA  N  logchange lowCIlogfitnesschange
## 1    Cherry  CE1  Cherry          7  1 10 -0.2740368           -0.70210174
## 2    Cherry  CE2  Cherry          7  1  4 -0.5617188           -1.21534852
## 3    Cherry  CE3  Cherry          7  1 13  0.1852825           -0.18979611
## 4    Cherry  CE4  Cherry          7  1  5  0.1279134           -0.43781493
## 5    Cherry  CEA  Cherry         29  1 30  0.1476700           -0.12317382
## 6    Cherry  CEB  Cherry         29  1 30  0.2503803           -0.01964191
##   upCIlogfitnesschange logfecundchange lowCIlogfecundchange upCIlogfecundchange
## 1            0.1969999       0.2174413         -0.008054728           0.4563722
## 2            0.2019585       0.5534521          0.216090324           0.9271423
## 3            0.5938166       0.1891403         -0.012370462           0.4006261
## 4            0.7888315       0.2990560         -0.007390043           0.6339803
## 5            0.4301216      -0.3337778         -0.479544492          -0.1846294
## 6            0.5320919      -0.3829105         -0.528887054          -0.2335638
##   logeggtoadchange lowCIlogeggtoadchange upCIlogeggtoadchange
## 1     -0.494165542            -0.6522000         -0.341712049
## 2     -1.115170967            -1.3960146         -0.855075837
## 3     -0.003857865            -0.1201706          0.110148034
## 4     -0.171142635            -0.3556475          0.006047628
## 5      0.481447806             0.3949970          0.567185941
## 6      0.633290781             0.5490961          0.716914363

1.3 Formatting dataset

#Formatting for plotting pairwise of fitness change
EC_Cran_Cher_G7<-formattingplotpair(data_logchange, "7", "Cherry", "Cranberry")
EC_Straw_Cran_G7<-formattingplotpair(data_logchange, "7", "Cranberry", "Strawberry")
EC_Straw_Cher_G7<-formattingplotpair(data_logchange, "7", "Strawberry", "Cherry")
EC_Cran_Cher_G29<-formattingplotpair(data_logchange, "29", "Cherry", "Cranberry")
EC_Straw_Cran_G29<-formattingplotpair(data_logchange, "29", "Cranberry", "Strawberry")
EC_Straw_Cher_G29<-formattingplotpair(data_logchange, "29", "Strawberry", "Cherry")


formattinglogchange <- function(logchange_dataset = data_logchange, generation="7", 
                                          fruitcomb=c("Cherry", "Cranberry"), trait="fecundity"){
## Extract sympatric combinations
TEMP_symp <- logchange_dataset[logchange_dataset$Generation==generation&
                                 logchange_dataset$Treatment%in%fruitcomb&
                                 logchange_dataset$Fruit_s%in%fruitcomb&logchange_dataset$SA==1,]
names(TEMP_symp)[6:ncol(TEMP_symp)] <- paste0(names(TEMP_symp)[6:ncol(TEMP_symp)], "_symp")
## Sort dataset
TEMP_symp <- TEMP_symp[order(TEMP_symp$Line),]

## Extract allopatric combinations
TEMP_allop <- logchange_dataset[logchange_dataset$Generation==generation&
                                  logchange_dataset$Treatment%in%fruitcomb&
                                  logchange_dataset$Fruit_s%in%fruitcomb&logchange_dataset$SA==0,]
names(TEMP_allop)[6:ncol(TEMP_allop)] <- paste0(names(TEMP_allop)[6:ncol(TEMP_allop)], "_allop")
## Sort dataset
TEMP_allop <- TEMP_allop[order(TEMP_allop$Line),]

  ## Combine datasets
if(identical(TEMP_symp$Line, TEMP_allop$Line)){
  if(trait == "fitness"){
    TEMP <- data.frame(TEMP_symp[, c(1:4, 6:9)], TEMP_allop[, 6:9])
    TEMP$N_sumsympallop <- TEMP$N_symp + TEMP$N_allop
  }else{
    if (trait == "fecundity") {
    TEMP <- data.frame(TEMP_symp[, c(1:4, 6, 10:12)], TEMP_allop[, c(6, 10:12)])
    TEMP$N_sumsympallop <- TEMP$N_symp + TEMP$N_allop
    }else{
      if (trait == "eggtoad") {
      TEMP <- data.frame(TEMP_symp[, c(1:4, 6, 13:15)], TEMP_allop[, c(6, 13:15)])
      TEMP$N_sumsympallop <- TEMP$N_symp + TEMP$N_allop
      }else{
      print("Error: trait unknown")
      }
    }
  }

  }else{
    print("Error: sympatric and allopatric datasets are not in the same order")
 }
    
 return(TEMP)
}


#Formatting for plot pool
TEMP_dataG29_CheCran <- formattinglogchange(data_logchange, "29", fruitcomb=c("Cherry", "Cranberry"), trait="fitness")
TEMP_dataG29_CranStraw <- formattinglogchange(data_logchange, "29", fruitcomb=c("Cranberry", "Strawberry"), trait="fitness")
TEMP_dataG29_StrawChe <- formattinglogchange(data_logchange, "29", fruitcomb=c("Strawberry", "Cherry"), trait="fitness")


## Data longitudinal 
data_sum_allfruits <- loadlongitudinaldata_allfruits(dataset = "DATA_Adults_G1G29.csv", rm_generation_max = 5)
data_sum_allfruits_AN<-data_sum_allfruits[data_sum_allfruits$Generation!=1,]

2 SUP: Pairwise fitness change

2.1 Plot

#Limit axis 
lim_G7<-max(abs(min(data_logchange[data_logchange$Generation=="7",]$lowCIlogfitnesschange, na.rm = T)),
            max(data_logchange[data_logchange$Generation=="7",]$upCIlogfitnesschange, na.rm = T))

lim_G29<-max(abs(min(data_logchange[data_logchange$Generation=="29",]$upCIlogfitnesschange, na.rm = T)),
            max(data_logchange[data_logchange$Generation=="29",]$upCIlogfitnesschange, na.rm = T))

#Plot
PAIR_StrawCran_G7<-ggplot(data = EC_Straw_Cran_G7,
                          aes(x = logchange_Strawberry,y = logchange_Cranberry, color=Fruit_s)) +
  geom_errorbar(aes(ymin = lowCIlogfitnesschange_Cranberry,
                    ymax = upCIlogfitnesschange_Cranberry),
                width=0.02,size=0.2,alpha=1) + 
  geom_errorbarh(aes(xmin = lowCIlogfitnesschange_Strawberry,
                     xmax = upCIlogfitnesschange_Cranberry),
                 height=0.02,size=0.2,alpha=1) + 
  geom_point(shape=21, size=3,  fill = "#ffffff", stroke=1.3) + 
  xlab("Fitness change\nin strawberry")  +     
  ylab("Fitness change\nin cranberry")  +
  xlim(-lim_G7,lim_G7) + 
  ylim(-lim_G7,lim_G7) +  
  scale_color_manual(values=c("#BC3C6D", "#FDB424", "#3FAA96"))  + 
  theme(axis.title.x = element_text(size=10, colour = "#3FAA96"),
        axis.title.y = element_text(size=10, colour = "#FDB424")) +
  theme_LO_sober
 PAIR_StrawCran_G7

PAIR_StrawChe_G7<-ggplot(data = EC_Straw_Cher_G7,
                         aes(x = logchange_Cherry,y = logchange_Strawberry, color=Fruit_s)) + 
  geom_errorbar(aes(ymin = lowCIlogfitnesschange_Strawberry,
                    ymax = upCIlogfitnesschange_Strawberry),
                  width=0.02, size=0.2, alpha=1) + 
  geom_errorbarh(aes(xmin = lowCIlogfitnesschange_Cherry,
                     xmax = upCIlogfitnesschange_Cherry),
                  height=0.02, size=0.2, alpha=1) + 
  geom_point(shape=21, size=3,  fill = "#ffffff", stroke=1.3) + 
  ylab("Fitness change\nin strawberry")  + 
  xlab("Fitness change\nin cherry")  + 
  scale_color_manual(values=c("#BC3C6D", "#FDB424", "#3FAA96"))  + 
  xlim(-lim_G7,lim_G7) + 
  ylim(-lim_G7,lim_G7) +    
  theme(axis.title.x = element_text(size=10, colour = "#BC3C6D"),
           axis.title.y = element_text(size=10, colour = "#3FAA96")) +
  theme_LO_sober
 PAIR_StrawChe_G7

PAIR_CheCran_G7<-ggplot(data = EC_Cran_Cher_G7,aes(x = logchange_Cranberry,y = logchange_Cherry, 
                                          color=Fruit_s)) + 
  geom_errorbar(aes(ymin = lowCIlogfitnesschange_Cherry,
                      ymax = upCIlogfitnesschange_Cherry),
                  width=0.02, size=0.2, alpha=1) + 
  geom_errorbarh(aes(xmin = lowCIlogfitnesschange_Cranberry,
                       xmax = upCIlogfitnesschange_Cranberry),
                  height=0.02, size=0.2, alpha=1) + 
  geom_point(shape=21, size=3,  fill = "#ffffff", stroke=1.3) + 
  ylab("Fitness change\nin cherry")  + 
  xlab("Fitness change\nin cranberry")  + 
  xlim(-lim_G7, lim_G7) + 
  ylim(-lim_G7, lim_G7) +     
  scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96"))  + 
  theme_LO_sober + 
  theme(axis.title.x = element_text(colour = "#FDB424"),
        axis.title.y = element_text(colour = "#BC3C6D")) 
 PAIR_CheCran_G7

################## Final phenotyping step
#Plot
PAIR_CranStraw_G29<-ggplot(data = EC_Straw_Cran_G29,
                          aes(x = logchange_Strawberry, y = logchange_Cranberry,  color=Fruit_s)) + 
  geom_errorbar(aes(ymin = lowCIlogfitnesschange_Cranberry,
                      ymax = upCIlogfitnesschange_Cranberry),
                  width=0.02,size=0.2,alpha=1) + 
  geom_errorbarh(aes(xmin = lowCIlogfitnesschange_Strawberry,
                       xmax = upCIlogfitnesschange_Strawberry),
                  height=0.02,size=0.2,alpha=1) + 
  geom_point(aes(fill = Fruit_s),shape=21, size=3) + 
  xlab("Fitness change\nin strawberry")  +     
  ylab("Fitness change\nin cranberry")  +
  xlim(-lim_G29,lim_G29) + 
  ylim(-lim_G29,lim_G29) +     
  scale_color_manual(values=c("#BC3C6D", "#FDB424", "#3FAA96"))  + 
  scale_fill_manual(values=c("#BC3C6D", "#FDB424", "#3FAA96"))  + 
  theme(axis.title.x = element_text(size=10, colour = "#3FAA96"),
        axis.title.y = element_text(size=10, colour = "#FDB424")) +
  theme_LO_sober
 PAIR_CranStraw_G29

PAIR_StrawChe_G29<-ggplot(data = EC_Straw_Cher_G29,
                         aes(x = logchange_Cherry,y = logchange_Strawberry,  color=Fruit_s)) + 
  geom_errorbar(aes(ymin = lowCIlogfitnesschange_Strawberry,
                      ymax = upCIlogfitnesschange_Strawberry),
                  width=0.02,size=0.2,alpha=1) + 
  geom_errorbarh(aes(xmin = lowCIlogfitnesschange_Cherry,
                       xmax = upCIlogfitnesschange_Cherry),
                  height=0.02,size=0.2,alpha=1) + 
  geom_point(aes(fill = Fruit_s),shape=21, size=3) + 
  ylab("Fitness change\nin strawberry")  + 
  xlab("Fitness change\nin cherry")  + 
  scale_color_manual(values=c("#BC3C6D", "#FDB424", "#3FAA96"))  + 
  scale_fill_manual(values=c("#BC3C6D", "#FDB424", "#3FAA96"))  + 
  xlim(-lim_G29,lim_G29) + 
  ylim(-lim_G29,lim_G29) +       
  theme(axis.title.x = element_text(colour = "#BC3C6D"),
           axis.title.y = element_text(colour = "#3FAA96")) +
  theme_LO_sober
 PAIR_StrawChe_G29

PAIR_CheCran_G29<-ggplot(data = EC_Cran_Cher_G29,
                         aes(x = logchange_Cranberry,y = logchange_Cherry, color=Fruit_s)) + 
  geom_errorbar(aes(ymin = lowCIlogfitnesschange_Cherry,
                      ymax = upCIlogfitnesschange_Cherry),
                  width=0.02,size=0.2,alpha=1) + 
  geom_errorbarh(aes(xmin = lowCIlogfitnesschange_Cranberry,
                       xmax = upCIlogfitnesschange_Cranberry),
                  height=0.02,size=0.2,alpha=1) + 
  geom_point(aes(fill = Fruit_s), shape=21, size=3) + 
  ylab("Fitness change\nin cherry")  + 
  xlab("Fitness change\nin cranberry")  + 
  xlim(-lim_G29,lim_G29) + 
  ylim(-lim_G29,lim_G29) +   
  scale_fill_manual(values=c("#BC3C6D", "#FDB424", "#3FAA96"))  + 
  scale_color_manual(values=c("#BC3C6D", "#FDB424", "#3FAA96"))  + 
  theme_LO_sober + 
  theme(axis.title.x = element_text(colour = "#FDB424"),
        axis.title.y = element_text(colour = "#BC3C6D")) 
 PAIR_CheCran_G29

 #Legend
TEMP_data_logchange_sum_Straw_Cher_G7<-data_logchange
TEMP_data_logchange_sum_Straw_Cher_G7$Pheno<-sample(c("Intermediate", "Final"),
                                                    length(data_logchange$logchange), 
                                                    replace=TRUE)

PAIR_plot_legend<- ggplot(data=TEMP_data_logchange_sum_Straw_Cher_G7, 
                          aes(x=Treatment, y=logchange, group=Fruit_s, colour=Fruit_s,shape=Pheno)) +
  geom_errorbar(aes(ymin=lowCIlogfitnesschange, ymax=upCIlogfitnesschange),
                  width=0.01,size=1) +
  geom_point(size=3, stroke=1.3) + 
  scale_shape_manual(name = "Phenotyping step", 
                     labels = c("Intermediate", "Final"),values=c(21,16)) +
  scale_color_manual(name= "Selection fruit", 
                     values=c("#BC3C6D", "#FDB424", "#3FAA96"), 
                     label=c("Cherry", "Cranberry", "Strawberry")) +   
  theme_LO_sober
PAIR_legend<-lemon::g_legend(PAIR_plot_legend)

rm(TEMP_data_logchange_sum_Straw_Cher_G7)




PAIR_Fig_Sup <-  cowplot::ggdraw() +
  cowplot::draw_plot(PAIR_CheCran_G7+theme(legend.position = "none"), 
            x =0.00, y = 0.5, width = 0.22, height =0.45) +
  cowplot::draw_plot(PAIR_StrawCran_G7+theme(legend.position = "none"), 
            x = 0.26, y = 0.5, width = 0.22, height = 0.45) +
  cowplot::draw_plot(PAIR_StrawChe_G7+theme(legend.position = "none"), 
            x = 0.52, y = 0.5, width = 0.22, height = 0.45) + 
  cowplot::draw_plot(PAIR_legend, x = 0.80, y = 0.5, width = 0.1, height = 0.1) +
  cowplot::draw_plot(PAIR_CheCran_G29+theme(legend.position = "none"), 
            x =0.00, y = 0, width = 0.22, height = 0.45) +
  cowplot::draw_plot(PAIR_CranStraw_G29+theme(legend.position = "none"), 
            x = 0.26, y = 0, width = 0.22, height = 0.45) +
  cowplot::draw_plot(PAIR_StrawChe_G29+theme(legend.position = "none"), 
            x = 0.52, y = 0, width = 0.22, height = 0.45) + 
  cowplot::draw_plot_label(c("Intermediate phenotyping step", "A", "B", "C", " ",
                    "Final phenotyping step", "D", "E", "F", " "),  
                  x = c(0.26,0,0.26,0.52,0.82,0.2,0,0.26,0.52,0.82), 
                  y = c(1.01,0.97,0.97,0.97,0.97,0.5,0.47, 0.47, 0.47, 0.47), 
                  hjust = c(-0.25,-0.25,-0.25,-0.25,-0.25,-0.75,-0.75,-0.75,-0.75,-0.75), 
                  vjust = c(1.5,1.5,1.5,1.5,1.5,1.5,1.5,1.5,1.5,1.5),
                  size = 14) 
 PAIR_Fig_Sup

cowplot::save_plot(file=here::here("figures", "SUPMAT_LogChange_Pairwise.pdf"), PAIR_Fig_Sup, 
                   base_height = 15/cm(1), base_width = 30/cm(1), dpi = 610)

3 SUP: Correlation between Nb_eggs and emergence_rate

3.1 Correlation

tapply(data_G0$Nb_eggs, data_G0$Treatment, mean)
##     Cherry  Cranberry Strawberry 
##     149.49     122.85     107.84
diag(tapply(data_G7$Nb_eggs, list(data_G7$Treatment,data_G7$Fruit_s), mean))
##     Cherry  Cranberry Strawberry 
##   195.4375   181.1591   174.2923
diag(tapply(data_G29$Nb_eggs, list(data_G29$Treatment,data_G29$Fruit_s), mean))
##     Cherry  Cranberry Strawberry 
##   103.7667   123.7000   126.7333
tapply(data_G0$Emergence_rate, data_G0$Treatment, mean)
##     Cherry  Cranberry Strawberry 
##  0.1837216  0.2660867  0.2777895
diag(tapply(data_G7$Emergence_rate, list(data_G7$Treatment,data_G7$Fruit_s), mean))
##     Cherry  Cranberry Strawberry 
##  0.1816565  0.2070999  0.2728989
diag(tapply(data_G29$Emergence_rate, list(data_G29$Treatment,data_G29$Fruit_s), mean))
##     Cherry  Cranberry Strawberry 
##  0.3421827  0.2794123  0.3543303
## Compute correlation by generation and selective fruit
data_logchangeNb_eggsEmergence_rate<-data.frame(data_logchange[,c(1:6)],
                                                Nb_eggslogchange=data_logchange[,"logfecundchange"],
                                                Emergence_ratelogchange=data_logchange[,"logeggtoadchange"])
                                                
# plyr::ddply(data_logchangeNb_eggsEmergence_rate, .(Generation, Fruit_s), computecorrelation)
## WARNINGS: What is data_logchangeNb_eggsEmergence_rate?
#?????????

3.2 Plot

#Formating data
ymin = -50
ymax = 50

##################################################
##################   G7
# Limits

vec_minmax <- function (data_set = data_logchange, gen = "7", fruit ="Cherry") {
ymin_PAIR_Che_G7=min(min(data_logchange[data_logchange$Generation==gen&
                                             data_logchange$Fruit_s==fruit,]$lowCIlogfecundchange, na.rm= TRUE),
                min(data_logchange[data_logchange$Generation==gen&
                                             data_logchange$Fruit_s==fruit,]$lowCIlogeggtoadchange, na.rm= TRUE)) 
ymax_PAIR_Che_G7=max(max(data_logchange[data_logchange$Generation==gen&
                                             data_logchange$Fruit_s==fruit,]$upCIlogfecundchange, na.rm= TRUE),
                max(data_logchange[data_logchange$Generation==gen&
                                             data_logchange$Fruit_s==fruit,]$upCIlogeggtoadchange, na.rm= TRUE))
vec_min_max<-c(ymin_PAIR_Che_G7,ymax_PAIR_Che_G7)
return(vec_min_max)
}


# Plot
PAIR_Che_G7 <- ggplot(data = data_logchange[data_logchange$Generation=="7"&
                                             data_logchange$Fruit_s=="Cherry",]) + 
  geom_errorbar(aes(x = logfecundchange, 
                    ymin = lowCIlogeggtoadchange,
                    ymax = upCIlogeggtoadchange,
                    color = Fruit_s),
                width= 0.02, size = 0.3, alpha = 0.8) + 
  geom_errorbarh(aes(y = logeggtoadchange, 
                     xmin = lowCIlogfecundchange, 
                     xmax = upCIlogfecundchange, 
                     color = Fruit_s),
                 height = 0.02, size = 0.3, alpha = 0.8) + 
  geom_point(aes(x = logfecundchange, y = logeggtoadchange,  
                 color = Fruit_s, fill = Fruit_s, shape = Treatment),
             size = 3, fill = "white", stroke =1.2) + 
  xlab("Log fecundity change between\nintermediate and initial phenotyping steps")  + 
  ylab("Logit egg-to-adult viability\nchange between intermediate\nand initial phenotyping steps")  + 
  ggtitle("Evolved on Cherry") + 
  theme_LO_sober + theme(plot.title = element_text(color = "#BC3C6D")) +
  labs(shape = "Test fruit", color = "Evolution fruit") + 
  scale_shape_manual(values = c(21, 22, 24)) + 
  scale_color_manual(values = c("#BC3C6D"))  + 
  coord_cartesian(ylim = vec_minmax(data_logchange,"7","Cherry"), 
                  xlim = vec_minmax(data_logchange,"7","Cherry")) 
  
 PAIR_Che_G7

# Limits
PAIR_Cran_G7 <- ggplot(data = data_logchange[data_logchange$Generation=="7"&
                                             data_logchange$Fruit_s=="Cranberry",]) + 
  geom_errorbar(aes(x = logfecundchange, 
                    ymin = lowCIlogeggtoadchange,
                    ymax = upCIlogeggtoadchange,
                    color = Fruit_s),
                width= 0.02, size = 0.3, alpha = 0.8) + 
  geom_errorbarh(aes(y = logeggtoadchange, 
                     xmin = lowCIlogfecundchange, 
                     xmax = upCIlogfecundchange, 
                     color = Fruit_s),
                 height = 0.02, size = 0.3, alpha = 0.8) + 
  geom_point(aes(x = logfecundchange, y = logeggtoadchange,  
                 color = Fruit_s, fill = Fruit_s, shape = Treatment),
             size = 3, fill = "white", stroke =1.2) + 
  xlab("Log fecundity change between\nintermediate and initial phenotyping steps")  + 
  ylab("Logit egg-to-adult viability change between\nintermediate and initial phenotyping steps")  + 
  ggtitle("Evolved on Cranberry") + 
  theme_LO_sober + theme(plot.title = element_text(color = "#FDB424")) +
  labs(shape = "Test fruit", color = "Evolution fruit") + 
  scale_shape_manual(values = c(21, 22, 24)) + 
  scale_color_manual(values = c("#FDB424"))  + 
  coord_cartesian(ylim = vec_minmax(data_logchange,"7","Cranberry"), 
                  xlim = vec_minmax(data_logchange,"7","Cranberry")) 
  
 PAIR_Cran_G7

# Limits
# Limits
PAIR_Straw_G7 <- ggplot(data = data_logchange[data_logchange$Generation=="7"&
                                             data_logchange$Fruit_s=="Strawberry",]) + 
  geom_errorbar(aes(x = logfecundchange, 
                    ymin = lowCIlogeggtoadchange,
                    ymax = upCIlogeggtoadchange,
                    color = Fruit_s),
                width= 0.02, size = 0.3, alpha = 0.8) + 
  geom_errorbarh(aes(y = logeggtoadchange, 
                     xmin = lowCIlogfecundchange, 
                     xmax = upCIlogfecundchange, 
                     color = Fruit_s),
                 height = 0.02, size = 0.3, alpha = 0.8) + 
  geom_point(aes(x = logfecundchange, y = logeggtoadchange,  
                 color = Fruit_s, fill = Fruit_s, shape = Treatment),
             size = 3, fill = "white", stroke =1.2) + 
  xlab("Log fecundity change between\nintermediate and initial phenotyping steps")  + 
  ylab("Logit egg-to-adult viability change between\nintermediate and initial phenotyping steps")  + 
  ggtitle("Evolved on Strawberry") + 
  theme_LO_sober + theme(plot.title = element_text(color = "#3FAA96")) +
  labs(shape = "Test fruit", color = "Evolution fruit") + 
  scale_shape_manual(values = c(21, 22, 24)) + 
  scale_color_manual(values = c("#3FAA96"))  + 
  coord_cartesian(ylim = vec_minmax(data_logchange,"7","Strawberry"), 
                  xlim = vec_minmax(data_logchange,"7","Strawberry")) 
  
 PAIR_Straw_G7

#####################################################################################
##################################      G29        ##################################
#####################################################################################
# Plot
PAIR_Che_G29 <- ggplot(data = data_logchange[data_logchange$Generation=="29"&
                                             data_logchange$Fruit_s=="Cherry",]) + 
  geom_errorbar(aes(x = logfecundchange, 
                    ymin = lowCIlogeggtoadchange,
                    ymax = upCIlogeggtoadchange,
                    color = Fruit_s),
                width= 0.02, size = 0.3, alpha = 0.8) + 
  geom_errorbarh(aes(y = logeggtoadchange, 
                     xmin = lowCIlogfecundchange, 
                     xmax = upCIlogfecundchange, 
                     color = Fruit_s),
                 height = 0.02, size = 0.3, alpha = 0.8) + 
  geom_point(aes(x = logfecundchange, y = logeggtoadchange,  
                 color = Fruit_s, fill = Fruit_s, shape = Treatment),
             size = 3, fill = "white", stroke =1.2) + 
  xlab("Log fecundity change\nbetween final and\ninitial phenotyping steps")  + 
  ylab("Logit egg-to-adult viability\nchange between final and\ninitial phenotyping steps")  + 
  ggtitle("Evolved on Cherry") + 
  theme_LO_sober + theme(plot.title = element_text(color = "#BC3C6D")) +
  labs(shape = "Test fruit", color = "Evolution fruit") + 
  scale_shape_manual(values = c(16, 15, 17)) + 
  scale_color_manual(values = c("#BC3C6D"))  + 
  coord_cartesian(ylim = vec_minmax(data_logchange,"29","Cherry"), 
                  xlim = vec_minmax(data_logchange,"29","Cherry")) 
  
 PAIR_Che_G29

# Limits
PAIR_Cran_G29 <- ggplot(data = data_logchange[data_logchange$Generation=="29"&
                                             data_logchange$Fruit_s=="Cranberry",]) + 
  geom_errorbar(aes(x = logfecundchange, 
                    ymin = lowCIlogeggtoadchange,
                    ymax = upCIlogeggtoadchange,
                    color = Fruit_s),
                width= 0.02, size = 0.3, alpha = 0.8) + 
  geom_errorbarh(aes(y = logeggtoadchange, 
                     xmin = lowCIlogfecundchange, 
                     xmax = upCIlogfecundchange, 
                     color = Fruit_s),
                 height = 0.02, size = 0.3, alpha = 0.8) + 
  geom_point(aes(x = logfecundchange, y = logeggtoadchange,  
                 color = Fruit_s, fill = Fruit_s, shape = Treatment),
             size = 3, fill = "white", stroke =1.2) + 
  xlab("Log fecundity change between\nfinal and initial phenotyping steps")  + 
  ylab("Logit egg-to-adult viability change between\nfinal and initial phenotyping steps")  + 
  ggtitle("Evolved on Cranberry") + 
  theme_LO_sober + theme(plot.title = element_text(color = "#FDB424")) +
  labs(shape = "Test fruit", color = "Evolution fruit") + 
  scale_shape_manual(values = c(16, 15, 17)) + 
  scale_color_manual(values = c("#FDB424"))  + 
  coord_cartesian(ylim = vec_minmax(data_logchange,"29","Cranberry"), 
                  xlim = vec_minmax(data_logchange,"29","Cranberry")) 
  
 PAIR_Cran_G29

# Limits
# Limits
PAIR_Straw_G29 <- ggplot(data = data_logchange[data_logchange$Generation=="29"&
                                             data_logchange$Fruit_s=="Strawberry",]) + 
  geom_errorbar(aes(x = logfecundchange, 
                    ymin = lowCIlogeggtoadchange,
                    ymax = upCIlogeggtoadchange,
                    color = Fruit_s),
                width= 0.02, size = 0.3, alpha = 0.8) + 
  geom_errorbarh(aes(y = logeggtoadchange, 
                     xmin = lowCIlogfecundchange, 
                     xmax = upCIlogfecundchange, 
                     color = Fruit_s),
                 height = 0.02, size = 0.3, alpha = 0.8) + 
  geom_point(aes(x = logfecundchange, y = logeggtoadchange,  
                 color = Fruit_s, fill = Fruit_s, shape = Treatment),
             size = 3, fill = "white", stroke =1.2) + 
  xlab("Log fecundity change between\nfinal and initial phenotyping steps")  + 
  ylab("Logit egg-to-adult viability change between\nfinal and initial phenotyping steps")  + 
  ggtitle("Evolved on Strawberry") + 
  theme_LO_sober + theme(plot.title = element_text(color = "#3FAA96")) +
  labs(shape = "Test fruit", color = "Evolution fruit") + 
  scale_shape_manual(values = c(16, 15, 17)) + 
  scale_color_manual(values = c("#3FAA96"))  + 
  coord_cartesian(ylim = vec_minmax(data_logchange,"29","Strawberry"), 
                  xlim = vec_minmax(data_logchange,"29","Strawberry")) 
  
 PAIR_Straw_G29

legend_tradevide <-  ggplot(data = data_logchange[data_logchange$Generation == "29",],
                          aes(x =logchange, y = logchange, fill = Fruit_s, 
                              shape = Treatment)) + 
  geom_point(size =2.5, fill = "white") + 
  labs(shape = "Test fruit") + 
  scale_shape_manual(values = c(16, 15, 17)) + 
  theme_LO_sober

legend_trade <- lemon::g_legend(legend_tradevide)

 
 

CORRELATION_BETWEEN_TRAITS <- cowplot::ggdraw() + 
  cowplot::draw_plot(PAIR_Che_G7 + theme(legend.position = "none",
                                         axis.title.y = element_text(size=12),
                                         axis.title.x = element_blank()), 
            x = 0.01, y = 0.53, width = 0.3, height = 0.41) + 
  cowplot::draw_plot(PAIR_Cran_G7 + theme(legend.position = "none",   
                                         axis.title.x = element_blank(),
                                         axis.title.y = element_blank()), 
            x = 0.34, y = 0.53, width = 0.26, height = 0.41) + 
  cowplot::draw_plot(PAIR_Straw_G7+ theme(legend.position = "none", 
                                         axis.title.x = element_blank(), 
                                         axis.title.y = element_blank()), 
            x = 0.61, y = 0.53, width = 0.26, height = 0.41) + 
  cowplot::draw_plot(legend_trade, x = 0.89, y = 0.5, width = 0.1, height = 0.1) + 
  cowplot::draw_plot(PAIR_Che_G29 + theme(legend.position = "none",
                                         axis.title.y = element_text(size=12), 
                                         axis.title.x = element_blank()), 
            x = 0.01, y = 0.03, width = 0.3, height = 0.41) + 
  cowplot::draw_plot(PAIR_Cran_G29 + theme(legend.position = "none", 
                                         axis.title.x = element_blank(),
                                         axis.title.y = element_blank()), 
            x = 0.34, y = 00.03, width = 0.26, height = 0.41) + 
  cowplot::draw_plot(PAIR_Straw_G29 + theme(legend.position = "none", 
                                         axis.title.x = element_blank(), 
                                         axis.title.y = element_blank()), 
            x = 0.61, y = 00.03, width = 0.26, height = 0.41) + 
  cowplot::draw_plot_label(c("Intermediate phenotyping step", "A", " ", " ", " ",
                    "Final phenotyping step", "B", " ", " ", " ", 
                    "Log fecundity change between final and initial phenotyping steps", 
                    "Log fecundity change between intermediate and initial phenotyping steps"),  
                  x = c(0.28, 0.01, 0.34, 0.61, 0.92, 0.230, 0.01, 0.30, 0.61, 0.92,0.15,0.1), 
                  y = c(0.99, 0.95, 0.95, 0.95, 0.95, 0.49, 0.45, 0.45, 0.45, 0.45, 0.05, 0.55), 
                  hjust = c(-0.25, -0.25, -0.25, -0.25, -0.25, -0.75, -0.75, -0.75, -0.75, -0.75, -0.25,  -0.25), 
                  vjust = c(1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5),
                  size = c(rep(16,10),12.5,12.5), 
                  fontface = c(rep("bold",10),"plain","plain")) 
 CORRELATION_BETWEEN_TRAITS

#  
# #  
# 
cowplot::save_plot(file =here::here("figures", "SUPMAT_Correlation_between_Traits.pdf"),
                   CORRELATION_BETWEEN_TRAITS,
                   base_height = 16/cm(1), base_width = 26/cm(1), dpi = 610)

4 A SUPR: Long vs Fitness

4.1 A SUPR

#To consider fruit maternal effect and GF maternal effect: new plot 
## Y-axis = log change = log(Fitness pheno)-log(fitness initial)
## X-axis = fitness temp / fitness initial
 ## example: for CEA during first phase 
    ## log(mean (CE1 from G2 to G5) / mean (Cherry G2))

##To check with Nico: for x-axis: denominator=mean Cherry G2 or mean CE1 G2 (???????????)


####### Phenotyping steps 
#Remove allopatric data
data_fitness_pheno <- data_logchange[data_logchange$SA==1,]
data_fitness_pheno <- data_fitness_pheno[, c("Line", "Fruit_s", "logchange", "lowCIlogfitnesschange", "upCIlogfitnesschange")]


####### Longitudinal data 
#Compute log change
data_logchange_long <- computelogchange_forlongdata(longitudinal_dataset = data_sum)


####### Merge data
data_compare_longitudinal_pheno <- merge(data_logchange_long,data_fitness_pheno,by=c("Line", "Fruit_s"))
data_compare_longitudinal_pheno <- droplevels(data_compare_longitudinal_pheno)

## Does not seem to be a correlation between measures
cor.test(data_compare_longitudinal_pheno$logchange[data_compare_longitudinal_pheno$Phase=="first_prepool"], data_compare_longitudinal_pheno$logchange_long[data_compare_longitudinal_pheno$Phase=="first_prepool"])

cor.test(data_compare_longitudinal_pheno$logchange[data_compare_longitudinal_pheno$Phase=="second_postpool"], data_compare_longitudinal_pheno$logchange_long[data_compare_longitudinal_pheno$Phase=="second_postpool"])


####### Geary test
#Lines: CE2 and CR2 do not pass Geary's test (the threshold of a standardized mean greater than 3)
data_compare_longitudinal_pheno$Line_type  <- ifelse(data_compare_longitudinal_pheno$Line == "CR2"|
                                                       data_compare_longitudinal_pheno$Line == "CE2", "solid","dashed")

4.2 A SUPR Plot long vs fitness

PLOT_IntermediatePheno_LOGCHANGE<-ggplot(data=data_compare_longitudinal_pheno[data_compare_longitudinal_pheno$Phase=="first_prepool",], 
       aes(y=logchange,x=logchange_long, color=Fruit_s,shape=Fruit_s, linetype=Line_type)) +
  geom_errorbar(aes(x = logchange_long, 
                    ymin = logchange-(1.96*sd_logchange), 
                    ymax = logchange+(1.96*sd_logchange)),
                  width=0.02,size=0.2,alpha=1) +
  geom_errorbarh(aes(y = logchange, 
                    xmin = logchange_long-1.96*sd_logchange_long,
                    xmax = logchange_long+1.96*sd_logchange_long),
                 height=0.02,size=0.2,alpha=1) +
  geom_point(size=3, fill = "#ffffff", stroke=1.3) +
  scale_color_manual(values=c("#BC3C6D", "#FDB424", "#3FAA96")) + 
  scale_shape_manual(values=c(21, 22, 24)) + 
  xlab("Fitness change between average fitness across generations 2 to 5\nand average fitness across generations 2 to 5") + 
  ylab("Fitness change between\nintermediate and initial phenotyping steps") + 
  labs(color="Selection fruit", shape="Selection fruit") + 
  geom_abline(intercept = 0, slope = 1, color="black", linetype="dashed", size=0.8) +
  ylim(-1.9,0.9) + 
  xlim(-1.9,0.9) +
  guides(linetype=FALSE) +
  ggtitle("Intermediate phenotyping step vs. Phase 1") +
  theme_LO_sober 
PLOT_IntermediatePheno_LOGCHANGE 


PLOT_FinalPheno_LOGCHANGE <- 
  ggplot(data=data_compare_longitudinal_pheno
                        [data_compare_longitudinal_pheno$Phase=="second_postpool",], 
                        aes(y=logchange, x=logchange_long, 
                            color=Fruit_s, shape=Fruit_s, fill=Fruit_s, linetype=Line_type)) +
  geom_errorbar(aes(x = logchange_long, 
                    ymin = logchange-(1.96*sd_logchange),
                    ymax = logchange+(1.96*sd_logchange)),
                  width=0.02,size=0.2,alpha=1) +
  geom_errorbarh(aes(y = logchange, 
                    xmin = logchange_long-1.96*sd_logchange_long,
                    xmax = logchange_long+1.96*sd_logchange_long),
                 height=0.02,size=0.2,alpha=1) +
  geom_abline(intercept = 0, slope = 1, color="black", 
                 linetype="dashed", size=0.8) +
  geom_point(size=3, stroke=1) +
  scale_fill_manual(values=c("#BC3C6D", "#FDB424", "#3FAA96")) + 
  scale_color_manual(values=c("#BC3C6D", "#FDB424", "#3FAA96")) + 
  scale_shape_manual(values=c(21, 22, 24)) + 
  xlab("Fitness change between average fitness across generations 13 to 26\nand average fitness across generations 2 to 5") + 
  ylab("Fitness change between\nfinal and initial phenotyping steps") + 
  labs(color="Selection fruit", shape="Selection fruit", fill="Selection fruit") + 
  ylim(-0.5,1.7) +
  xlim(-0.5,1.7) +
  guides(linetype=FALSE) +
  ggtitle("Final phenotyping step vs. Phase 3") +
  theme_LO_sober
PLOT_FinalPheno_LOGCHANGE  

    

PLOT_Comparison_Pheno_Longitudinal_LOGCHANGE <- cowplot::ggdraw() +
  cowplot::draw_plot(PLOT_IntermediatePheno_LOGCHANGE + 
                       theme(axis.title.y = element_text(size=10),
                             axis.title.x = element_text(size=10)),
            x = 0, y = 0.5, width = 1, height = 0.5) +
  cowplot::draw_plot(PLOT_FinalPheno_LOGCHANGE + 
                       theme(axis.title.y = element_text(size=10),
                             axis.title.x = element_text(size=10)),
            x =0, y = 0, width = 1, height = 0.5) +
  cowplot::draw_plot_label(c("A", "B"),  
                  x = c(0,0), y = c(1,0.5), 
                  size = 14) 
PLOT_Comparison_Pheno_Longitudinal_LOGCHANGE


cowplot::save_plot(file=here::here("figures", "SUPMAT_LogChange_Pheno_Longitudinal.pdf"),
                   PLOT_Comparison_Pheno_Longitudinal_LOGCHANGE, base_height = 20/cm(1), base_width = 15/cm(1), dpi = 1200)

4.3 A SUPR Extra plot Maternal effects

m <- lm(logchange ~ logchange_long + offset(logchange_long),
        data = data_compare_longitudinal_pheno[data_compare_longitudinal_pheno$Phase=="second_postpool",]) 

summary(m)
#pval of logchange_long = 0.003 => slope is different of 1 
#pval of Intercept = 0.80 => no evolution of maternal effect?


## Ratio maternal effects derived pop / maternal effect ancestral population 
exp(coef(m)[1]) 

# proportion of adaptation due to maternal effects
data_compare_longitudinal_pheno$mateff <- (data_compare_longitudinal_pheno$logchange_long -
  data_compare_longitudinal_pheno$logchange) / data_compare_longitudinal_pheno$logchange_long

data_compare_longitudinal_pheno


#Plot
PLOT_Maternaleffects<-ggplot(data=data_compare_longitudinal_pheno, 
       aes(y=mateff,x=logchange_long, color=Fruit_s, shape=Fruit_s, fill=interaction(Phase,Fruit_s))) +
  geom_point(size=3,  stroke=1.3) +
  scale_fill_manual(name = "Phase", 
                      values = c("#ffffff","#BC3C6D", "#ffffff","#FDB424", "#ffffff", "#3FAA96"), 
                      breaks = c("first_prepool.Cherry", "second_postpool.Cherry"), 
                      labels = c("Phase 1","Phase 3")) +
  scale_color_manual(values=c("#BC3C6D", "#FDB424", "#3FAA96")) + 
  scale_shape_manual(values=c(21, 22, 24)) + 
  xlab("Fitness change between average fitness\nacross generations 2 to 5 (13 to 26) and generation 2") + 
  ylab("Proportion of adaptation\ndue to evolution of maternal effects") + 
  labs(color="Selection fruit", shape="Selection fruit") + 
  guides(fill = guide_legend(override.aes = list(fill = c("white","black"), shape=21))) +
  theme_LO_sober 
PLOT_Maternaleffects 


cowplot::save_plot(file=here::here("figures", "SUPMAT_Maternal_effects.pdf"),
                   PLOT_Maternaleffects, base_height = 20/cm(1), base_width = 15/cm(1), dpi = 1200)

5 SUP: Adaptation

5.1 Analysis Phase 1 (8 fruits)

## Check random effect structure
mod1 <- lme4::lmer(fitness ~ Line*Generation + (1|Generation:Fruit_s), 
            weights = N, data = data_sum_allfruits_AN, REML = TRUE)
mod2 <- lme4::lmer(fitness ~ Line*Generation + (1|Generation) + (1|Generation:Fruit_s), 
            weights = N, data = data_sum_allfruits_AN, REML = TRUE)
MuMIn::model.sel(mod1, mod2)
## Model selection table 
##       (Int)    Gnr Lin Gnr:Lin             family  random df  logLik  AICc
## mod1 0.9538 -1.548   +       + gaussian(identity)   G:F_s 71 -46.078 526.3
## mod2 1.0300 -1.580   +       + gaussian(identity) G+G:F_s 72 -46.060 545.3
##      delta weight
## mod1  0.00      1
## mod2 19.02      0
## Models ranked by AICc(x) 
## Random terms: 
## G:F_s = '1 | Generation:Fruit_s'
## G = '1 | Generation'
##Cl: the model with the same generation effect across lines does not increase the likelihood and does not provide a better fit to the data

#Models
mod_all_interaction <- lme4::lmer(fitness ~ Fruit_s*Generation + (1|Generation:Fruit_s), 
            weights = N, data = data_sum_allfruits_AN, REML = FALSE)

mod_all_generation <- lme4::lmer(fitness ~ Generation + (1|Generation:Fruit_s) , 
            weights = N, data = data_sum_allfruits_AN, REML = FALSE)

mod_all_fruit_generation <- lme4::lmer(fitness ~ Fruit_s+Generation + (1|Generation:Fruit_s) , 
            weights = N, data = data_sum_allfruits_AN, REML = FALSE)

mod_all_fruit <- lme4::lmer(fitness ~ Fruit_s + (1|Generation:Fruit_s) , 
            weights = N, data = data_sum_allfruits_AN, REML = FALSE)

mod_all_line <- lme4::lmer(fitness ~ Line + (1|Generation:Fruit_s), 
            weights = N, data = data_sum_allfruits_AN, REML = FALSE)

mod_all_null <- lme4::lmer(fitness ~ 1  + (1|Generation:Fruit_s), 
            weights = N, data = data_sum_allfruits_AN, REML = FALSE)

mod_all_interaction_line_generation <- lme4::lmer(fitness ~ Line*Generation + (1|Generation:Fruit_s), 
            weights = N, data = data_sum_allfruits_AN, REML = FALSE)

mod_line_generation <- lme4::lmer(fitness ~ Line+Generation + (1|Generation:Fruit_s), 
            weights = N, data = data_sum_allfruits_AN, REML = FALSE)

MuMIn::model.sel(mod_all_interaction,mod_all_fruit_generation,mod_all_fruit,mod_all_null,mod_all_generation,mod_all_line,mod_all_interaction_line_generation,mod_line_generation)
## Model selection table 
##                                       (Int) Frt_s      Gnr Frt_s:Gnr Lin
## mod_all_interaction                 -1.2580     + -0.56070         +    
## mod_all_fruit                       -2.4250     +                       
## mod_all_fruit_generation            -2.3690     + -0.02678              
## mod_all_line                        -2.3980                            +
## mod_line_generation                 -2.1860       -0.08907             +
## mod_all_generation                  -1.8340        0.27440              
## mod_all_null                        -0.9906                             
## mod_all_interaction_line_generation  0.9538       -1.54800             +
##                                     Gnr:Lin             family df   logLik
## mod_all_interaction                         gaussian(identity) 18  -80.610
## mod_all_fruit                               gaussian(identity) 10  -92.408
## mod_all_fruit_generation                    gaussian(identity) 11  -92.306
## mod_all_line                                gaussian(identity) 42  -37.016
## mod_line_generation                         gaussian(identity) 43  -35.790
## mod_all_generation                          gaussian(identity)  4 -112.481
## mod_all_null                                gaussian(identity)  3 -114.229
## mod_all_interaction_line_generation       + gaussian(identity) 71    6.826
##                                      AICc  delta weight
## mod_all_interaction                 205.0   0.00  0.681
## mod_all_fruit                       207.1   2.12  0.236
## mod_all_fruit_generation            209.4   4.40  0.076
## mod_all_line                        214.5   9.48  0.006
## mod_line_generation                 217.6  12.65  0.001
## mod_all_generation                  233.4  28.36  0.000
## mod_all_null                        234.7  29.70  0.000
## mod_all_interaction_line_generation 420.5 215.47  0.000
## Models ranked by AICc(x) 
## Random terms (all models): 
## '1 | Generation:Fruit_s'
summary(mod_all_interaction)
## Linear mixed model fit by maximum likelihood  ['lmerMod']
## Formula: fitness ~ Fruit_s * Generation + (1 | Generation:Fruit_s)
##    Data: data_sum_allfruits_AN
## Weights: N
## 
##      AIC      BIC   logLik deviance df.resid 
##    197.2    245.3    -80.6    161.2       89 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.8179 -0.7264  0.0873  0.6984  2.2653 
## 
## Random effects:
##  Groups             Name        Variance Std.Dev.
##  Generation:Fruit_s (Intercept) 0.000    0.000   
##  Residual                       1.813    1.346   
## Number of obs: 107, groups:  Generation:Fruit_s, 25
## 
## Fixed effects:
##                              Estimate Std. Error t value
## (Intercept)                   -1.2579     1.4340  -0.877
## Fruit_sCherry                  0.1344     1.4604   0.092
## Fruit_sCranberry               1.7136     1.4583   1.175
## Fruit_sFig                     1.1471     1.4909   0.769
## Fruit_sGrape                  -3.4559     3.1334  -1.103
## Fruit_sRosehips               -2.6107     1.6847  -1.550
## Fruit_sStrawberry              0.5629     1.4584   0.386
## Fruit_sTomato                 -1.5720     1.7459  -0.900
## Generation                    -0.5607     0.6930  -0.809
## Fruit_sCherry:Generation       0.7404     0.6988   1.060
## Fruit_sCranberry:Generation    0.2725     0.6979   0.391
## Fruit_sFig:Generation          0.2539     0.7097   0.358
## Fruit_sGrape:Generation        1.6698     1.5329   1.089
## Fruit_sRosehips:Generation     1.3420     0.8036   1.670
## Fruit_sStrawberry:Generation   0.6427     0.6980   0.921
## Fruit_sTomato:Generation       1.1111     0.8101   1.371
## convergence code: 0
## boundary (singular) fit: see ?isSingular
#Posthoc
mod_all_interaction <- lme4::lmer(fitness ~ Fruit_s*Generation + (1|Generation:Fruit_s), 
            weights = N, data = data_sum_allfruits_AN)
summary(mod_all_interaction)
## Linear mixed model fit by REML ['lmerMod']
## Formula: fitness ~ Fruit_s * Generation + (1 | Generation:Fruit_s)
##    Data: data_sum_allfruits_AN
## Weights: N
## 
## REML criterion at convergence: 184.6
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.59867 -0.66991  0.08051  0.64407  2.08904 
## 
## Random effects:
##  Groups             Name        Variance Std.Dev.
##  Generation:Fruit_s (Intercept) 0.000    0.00    
##  Residual                       2.132    1.46    
## Number of obs: 107, groups:  Generation:Fruit_s, 25
## 
## Fixed effects:
##                              Estimate Std. Error t value
## (Intercept)                   -1.2579     1.5550  -0.809
## Fruit_sCherry                  0.1344     1.5836   0.085
## Fruit_sCranberry               1.7136     1.5813   1.084
## Fruit_sFig                     1.1471     1.6166   0.710
## Fruit_sGrape                  -3.4559     3.3977  -1.017
## Fruit_sRosehips               -2.6107     1.8268  -1.429
## Fruit_sStrawberry              0.5629     1.5815   0.356
## Fruit_sTomato                 -1.5720     1.8931  -0.830
## Generation                    -0.5607     0.7515  -0.746
## Fruit_sCherry:Generation       0.7404     0.7577   0.977
## Fruit_sCranberry:Generation    0.2725     0.7568   0.360
## Fruit_sFig:Generation          0.2539     0.7695   0.330
## Fruit_sGrape:Generation        1.6698     1.6622   1.005
## Fruit_sRosehips:Generation     1.3420     0.8714   1.540
## Fruit_sStrawberry:Generation   0.6427     0.7569   0.849
## Fruit_sTomato:Generation       1.1111     0.8785   1.265
## convergence code: 0
## boundary (singular) fit: see ?isSingular
emt1 <- emmeans::emtrends(mod_all_interaction, "Fruit_s", var = "Generation")
emt1          ### estimated slopes of Generation for each level of Fruit_s
##  Fruit_s      Generation.trend     SE     df lower.CL upper.CL
##  Blackcurrant           -0.561 0.7580   83.7  -2.0682   0.9468
##  Cherry                  0.180 0.0978 1460.0  -0.0122   0.3715
##  Cranberry              -0.288 0.0904 1415.8  -0.4654  -0.1109
##  Fig                    -0.307 0.1684  606.9  -0.6375   0.0238
##  Grape                   1.109 1.4834  105.0  -1.8321   4.0504
##  Rosehips                0.781 0.4431  116.4  -0.0963   1.6589
##  Strawberry              0.082 0.0915 1262.4  -0.0974   0.2614
##  Tomato                  0.550 0.4584  107.6  -0.3583   1.4591
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95
pairs(emt1)   ### comparison of slopes
##  contrast                  estimate    SE     df t.ratio p.value
##  Blackcurrant - Cherry      -0.7404 0.764   85.8 -0.969  0.9776 
##  Blackcurrant - Cranberry   -0.2725 0.763   85.5 -0.357  1.0000 
##  Blackcurrant - Fig         -0.2539 0.777   88.9 -0.327  1.0000 
##  Blackcurrant - Grape       -1.6698 1.666  111.0 -1.002  0.9732 
##  Blackcurrant - Rosehips    -1.3420 0.878   92.0 -1.528  0.7902 
##  Blackcurrant - Strawberry  -0.6427 0.764   85.5 -0.842  0.9901 
##  Blackcurrant - Tomato      -1.1111 0.886   89.8 -1.254  0.9129 
##  Cherry - Cranberry          0.4678 0.133 1440.9  3.513  0.0108 
##  Cherry - Fig                0.4865 0.195  734.1  2.498  0.1977 
##  Cherry - Grape             -0.9295 1.487  105.8 -0.625  0.9985 
##  Cherry - Rosehips          -0.6016 0.454  124.9 -1.326  0.8874 
##  Cherry - Strawberry         0.0977 0.134 1364.1  0.729  0.9961 
##  Cherry - Tomato            -0.3707 0.469  114.9 -0.791  0.9933 
##  Cranberry - Fig             0.0187 0.191  713.6  0.098  1.0000 
##  Cranberry - Grape          -1.3973 1.486  105.7 -0.940  0.9813 
##  Cranberry - Rosehips       -1.0694 0.452  123.7 -2.365  0.2681 
##  Cranberry - Strawberry     -0.3701 0.129 1334.9 -2.879  0.0777 
##  Cranberry - Tomato         -0.8385 0.467  113.8 -1.795  0.6250 
##  Fig - Grape                -1.4160 1.493  107.1 -0.948  0.9804 
##  Fig - Rosehips             -1.0881 0.474  136.9 -2.295  0.3037 
##  Fig - Strawberry           -0.3888 0.192  702.2 -2.029  0.4627 
##  Fig - Tomato               -0.8572 0.488  125.1 -1.755  0.6511 
##  Grape - Rosehips            0.3278 1.548  107.6  0.212  1.0000 
##  Grape - Strawberry          1.0271 1.486  105.7  0.691  0.9971 
##  Grape - Tomato              0.5588 1.553  108.1  0.360  1.0000 
##  Rosehips - Strawberry       0.6993 0.452  123.7  1.546  0.7811 
##  Rosehips - Tomato           0.2309 0.638  112.1  0.362  1.0000 
##  Strawberry - Tomato        -0.4684 0.467  113.9 -1.002  0.9733 
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 8 estimates

5.2 Plot Phase 1 (8 fruits)

pd <- position_dodge(0.3) # move them .05 to the left and right

#Extract slope and intercept
dat_8fruits <- expand.grid(Generation=as.numeric(levels(as.factor(data_sum_allfruits_AN$Generation))),
                           Fruit_s=unique(data_sum_allfruits_AN$Fruit_s))
dat_8fruits$fitness_predicted <- predict(mod_all_interaction, newdata = dat_8fruits, 
                          re.form= NA, type = "response")





PLOT_CHERRY <- ggplot(data = data_sum_allfruits[data_sum_allfruits$Fruit_s == "Cherry",], 
                            aes(x = factor(Generation), group = Line, y = fitness, colour =Fruit_s)) + 
  geom_errorbar(aes(ymin = fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
                width=.1,position = pd, size = 0.4,color = "black") + 
  geom_line(size = 0.5,position = pd) + 
  geom_line(data = dat_8fruits[dat_8fruits$Fruit_s == "Cherry",],
                     aes(x = factor(Generation), y = fitness_predicted, colour = "black", group=NA), size = 0.7) +
  geom_point(size = 2, position = pd, shape = 21, fill = "white",stroke = 1.2) + 
  ylim(-5.65, 1.25) +
  ylab("Fitness") + 
  xlab("Generation") + 
  xlim("1","2","3","4","5") +
  scale_color_manual(values = c("black", "#BC3C6D")) + 
  ggtitle("Cherry") + 
  theme_LO_adaptation + theme(plot.title = element_text(color = "#BC3C6D"))
PLOT_CHERRY

PLOT_CRANBERRY <- ggplot(data = data_sum_allfruits[data_sum_allfruits$Fruit_s == "Cranberry",], 
                            aes(x = factor(Generation), group = Line, y = fitness, colour =Fruit_s)) + 
  geom_errorbar(aes(ymin = fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
                width=.1,position = pd, size = 0.4,color = "black") + 
  geom_line(size = 0.5,position = pd) + 
  geom_line(data = dat_8fruits[dat_8fruits$Fruit_s == "Cranberry",],
                     aes(x = factor(Generation), y = fitness_predicted, colour = "black", group=NA), size = 0.7) +
  geom_point(size = 2, position = pd, shape = 21, fill = "white",stroke = 1.2) + 
  ylim(-5.65, 1.25) +
  ylab("Fitness") + 
  xlim("1","2","3","4","5") +
  xlab("Generation") + 
  scale_color_manual(values = c("black","#FDB424")) + 
  ggtitle("Cranberry") + 
  theme_LO_adaptation + theme(plot.title = element_text(color = "#FDB424"))
PLOT_CRANBERRY

PLOT_STRAWBERRY <- ggplot(data = data_sum_allfruits[data_sum_allfruits$Fruit_s == "Strawberry",], 
                            aes(x = factor(Generation), group = Line, y = fitness, colour =Fruit_s)) + 
  geom_errorbar(aes(ymin = fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
                width=.1,position = pd, size = 0.4,color = "black") + 
  geom_line(size = 0.5,position = pd) + 
  geom_line(data = dat_8fruits[dat_8fruits$Fruit_s == "Strawberry",],
                     aes(x = factor(Generation), y = fitness_predicted, colour = "black", group=NA), size = 0.7) +
  geom_point(size = 2, position = pd, shape = 21, fill = "white",stroke = 1.2) + 
  ylim(-5.65, 1.25) +
  xlim("1","2","3","4","5") +
  ylab("Fitness") + 
  xlab("Generation") + 
  scale_color_manual(values = c("black","#3FAA96")) + 
  ggtitle("Strawberry") + 
  theme_LO_adaptation + theme(plot.title = element_text(color = "#3FAA96"))
PLOT_STRAWBERRY

PLOT_GRAPE <- ggplot(data = data_sum_allfruits[data_sum_allfruits$Fruit_s == "Grape",], 
                            aes(x = factor(Generation), group = Line, y = fitness, colour =Fruit_s)) + 
  geom_errorbar(aes(ymin = fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
                width=.1,position = pd, size = 0.4,color = "black") + 
  geom_line(size = 0.5,position = pd) + 
  geom_line(data = dat_8fruits[dat_8fruits$Fruit_s == "Grape"&dat_8fruits$Generation == c(2,3),],
                     aes(x = factor(Generation), y = fitness_predicted, colour = "black", group=NA), size = 0.7) +
  geom_point(size = 2, position = pd, shape = 21, fill = "white",stroke = 1.2) + 
  ylim(-5.65, 1.25) +
  xlim("1","2","3","4","5") +
  ylab("Fitness") + 
  xlab("Generation") + 
  scale_color_manual(values = c("black","#7B7554")) + 
  ggtitle("Grape") + 
  theme_LO_adaptation + theme(plot.title = element_text(color = "#7B7554"))
PLOT_GRAPE

PLOT_ROSEHIPS <- ggplot(data = data_sum_allfruits[data_sum_allfruits$Fruit_s == "Rosehips",], 
                            aes(x = factor(Generation), group = Line, y = fitness, colour =Fruit_s)) + 
  geom_errorbar(aes(ymin = fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
                width=.1,position = pd, size = 0.4,color = "black") + 
  geom_line(size = 0.5,position = pd) + 
  geom_line(data = dat_8fruits[dat_8fruits$Fruit_s == "Rosehips"&dat_8fruits$Generation != 5,],
                     aes(x = factor(Generation), y = fitness_predicted, colour = "black", group=NA), size = 0.7) +
  geom_point(size = 2, position = pd, shape = 21, fill = "white",stroke = 1.2) + 
  ylim(-5.65, 1.25) +
  xlim("1","2","3","4","5") +
  ylab("Fitness") + 
  xlab("Generation") + 
  scale_color_manual(values = c("black","#8ACDEA")) + 
  ggtitle("Rose hips") + 
  theme_LO_adaptation + theme(plot.title = element_text(color = "#8ACDEA"))
PLOT_ROSEHIPS

PLOT_TOMATO <- ggplot(data = data_sum_allfruits[data_sum_allfruits$Fruit_s == "Tomato",], 
                            aes(x = factor(Generation), group = Line, y = fitness, colour =Fruit_s)) + 
  geom_errorbar(aes(ymin = fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
                width=.1,position = pd, size = 0.4,color = "black") + 
  geom_line(size = 0.5,position = pd) + 
  geom_line(data = dat_8fruits[dat_8fruits$Fruit_s == "Tomato"&dat_8fruits$Generation != 5,],
                     aes(x = factor(Generation), y = fitness_predicted, colour = "black", group=NA), size = 0.7) +
  geom_point(size = 2, position = pd, shape = 21, fill = "white", stroke = 1.2) + 
  ylim(-5.65, 1.25) +
  xlim("1","2","3","4","5") +
  ylab("Fitness") + 
  xlab("Generation") + 
  scale_color_manual(values = c( "black","#ED6F47")) + 
  ggtitle("Tomato") + 
  theme_LO_adaptation + theme(plot.title = element_text(color = "#ED6F47"))
PLOT_TOMATO

PLOT_FIG <- ggplot(data = data_sum_allfruits[data_sum_allfruits$Fruit_s == "Fig",], 
                            aes(x = factor(Generation), group = Line, y = fitness, colour =Fruit_s)) + 
  geom_errorbar(aes(ymin = fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
                width=.1,position = pd, size = 0.4,color = "black") + 
  geom_line(size = 0.5,position = pd) + 
  geom_line(data = dat_8fruits[dat_8fruits$Fruit_s == "Fig"&dat_8fruits$Generation != 5,],
                     aes(x = factor(Generation), y = fitness_predicted, colour = "black", group=NA), size = 0.7) +
  geom_point(size = 2, position = pd, shape = 21, fill = "white", stroke = 1.2) + 
  ylim(-5.65, 1.25) +
  xlim("1","2","3","4","5") +
  ylab("Fitness") + 
  xlab("Generation") + 
  scale_color_manual(values = c("black","#93194F")) + 
  ggtitle("Fig") + 
  theme_LO_adaptation + theme(plot.title = element_text(color = "#93194F"))
PLOT_FIG

PLOT_BLACKCURRANT <- ggplot(data = data_sum_allfruits[data_sum_allfruits$Fruit_s == "Blackcurrant",], 
                            aes(x = factor(Generation), group = Line, y = fitness, colour =Fruit_s)) + 
  geom_errorbar(aes(ymin = fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
                width=.1,position = pd, size = 0.4,color = "black") + 
  geom_line(size = 0.5,position = pd) + 
  geom_line(data = dat_8fruits[dat_8fruits$Fruit_s == "Blackcurrant"&dat_8fruits$Generation == c(2,3),],
                     aes(x = factor(Generation), y = fitness_predicted, colour = "black", group=NA), size = 0.7) +
  geom_point(size = 2, position = pd, shape = 21, fill = "white", stroke = 1.2) + 
  ylim(-5.65, 1.25) +
  xlim("1","2","3","4","5") +
  ylab("Fitness") + 
  xlab("Generation") + 
  scale_color_manual(values = c( "black","#203753")) + 
  ggtitle("Blackcurrant") + 
  theme_LO_adaptation + theme(plot.title = element_text(color = "#203753"))
PLOT_BLACKCURRANT

DYNAMIC_EIGHT_JOIN <- cowplot::ggdraw() + 
  cowplot::draw_plot(PLOT_STRAWBERRY, x = 0, y = 0, width = 0.5, height = 0.28) + 
  cowplot::draw_plot(PLOT_GRAPE+ theme(axis.title.x = element_blank(),
                                              axis.text.x = element_blank(),
                                              axis.ticks.x = element_blank(),
                                              axis.line.x = element_blank()),
                     x = 0, y = 0.28, width = 0.5, height = 0.23) + 
  cowplot::draw_plot(PLOT_CRANBERRY+ theme(axis.title.x = element_blank(),
                                              axis.text.x = element_blank(),
                                              axis.ticks.x = element_blank(),
                                              axis.line.x = element_blank()),
                     x = 0, y = 0.53, width = 0.5, height = 0.23) + 
  cowplot::draw_plot(PLOT_BLACKCURRANT+ theme(axis.title.x = element_blank(),
                                              axis.text.x = element_blank(),
                                              axis.ticks.x = element_blank(),
                                              axis.line.x = element_blank()),
                     x = 0, y = 0.77, width = 0.5, height = 0.23) + 
  cowplot::draw_plot(PLOT_TOMATO+ theme(axis.title.y = element_blank()), 
                     x = 0.5, y = 0, width = 0.5, height = 0.28) + 
  cowplot::draw_plot(PLOT_ROSEHIPS+ theme(axis.title.y = element_blank(),
                                              axis.title.x = element_blank(),
                                              axis.text.x = element_blank(),
                                              axis.ticks.x = element_blank(),
                                              axis.line.x = element_blank()),
                     x = 0.5, y = 0.28, width = 0.5, height = 0.23) + 
  cowplot::draw_plot(PLOT_FIG+ theme(axis.title.y = element_blank(),
                                              axis.title.x = element_blank(),
                                              axis.text.x = element_blank(),
                                              axis.ticks.x = element_blank(),
                                              axis.line.x = element_blank()),
                     x = 0.5, y = 0.53, width = 0.5, height = 0.23) + 
  cowplot::draw_plot(PLOT_CHERRY + theme(axis.title.y = element_blank(),
                                              axis.title.x = element_blank(),
                                              axis.text.x = element_blank(),
                                              axis.ticks.x = element_blank(),
                                              axis.line.x = element_blank()),
                     x = 0.5, y = 0.77, width = 0.5, height = 0.23) +
  cowplot::draw_plot_label(c("A", "C",  "E", "G", "B",  "D", "F",  "H"),  
                  x = c(0, 0, 0, 0, 0.5, 0.5, 0.5, 0.5), 
                  y = c(1, 0.75, 0.50, 0.27, 1, 0.75, 0.5, 0.27), 
                  hjust = c(-0.5, -0.5, -0.5,-0.5, -0.5, -0.5, -0.5, -0.5), 
                  vjust = c(1.5, 1.5, 1.5,1.5, 1.5, 1.5,1.5, 1.5),
                  size = 12) 
 DYNAMIC_EIGHT_JOIN

cowplot::save_plot(file =here::here("figures", "SUPMAT_Fitness8Fruits.pdf"), DYNAMIC_EIGHT_JOIN, base_height = 17/cm(1), base_width = 15/cm(1), dpi = 1200)

5.3 Analysis PHASE 3

################### PHASE 3



## Test for random effects
mod0 <- lme4::lmer(fitness~ Generation*Line + (1|Generation:Fruit_s), 
            weights = N, data = data_sum[data_sum$Phase=="second_postpool",], REML = TRUE)
mod1 <- lme4::lmer(fitness~ Generation*Line + (1|Generation) + (1|Generation:Fruit_s), 
            weights = N, data = data_sum[data_sum$Phase=="second_postpool",], REML = TRUE)
MuMIn::model.sel(mod0, mod1)
## Model selection table 
##       (Int)      Gnr Lin Gnr:Lin             family  random df   logLik  AICc
## mod0 0.9619 -0.01430   +       + gaussian(identity)   G:F_s 24 -116.602 289.2
## mod1 0.9546 -0.01397   +       + gaussian(identity) G+G:F_s 25 -115.849 290.4
##      delta weight
## mod0  0.00  0.647
## mod1  1.21  0.353
## Models ranked by AICc(x) 
## Random terms: 
## G:F_s = '1 | Generation:Fruit_s'
## G = '1 | Generation'
## Cl: only a very slight increase in likelihood when adding the second random effect

## Test for fixed effects
mod1 <- lme4::lmer(fitness ~ 1 + (1|Generation:Fruit_s), 
            weights = N, data = data_sum[data_sum$Phase=="second_postpool",], REML = FALSE) 
mod2 <- lme4::lmer(fitness~ Line  + (1|Generation:Fruit_s), 
            weights = N, data = data_sum[data_sum$Phase=="second_postpool",], REML = FALSE)
mod3 <- lme4::lmer(fitness ~ Fruit_s  + (1|Generation:Fruit_s), 
            weights = N, data = data_sum[data_sum$Phase=="second_postpool",], REML = FALSE) 
mod4 <- lme4::lmer(fitness~ Generation*Line + (1|Generation:Fruit_s), 
            weights = N, data = data_sum[data_sum$Phase=="second_postpool",], REML = FALSE)
mod5 <- lme4::lmer(fitness~ Generation+Line + (1|Generation:Fruit_s), 
            weights = N, data = data_sum[data_sum$Phase=="second_postpool",], REML = FALSE)
mod6 <- lme4::lmer(fitness ~ Generation*Fruit_s + (1|Generation:Fruit_s), 
            weights = N, data = data_sum[data_sum$Phase=="second_postpool",], REML = FALSE) 
mod7 <- lme4::lmer(fitness ~ Generation+Fruit_s + (1|Generation:Fruit_s), 
            weights = N, data = data_sum[data_sum$Phase=="second_postpool",], REML = FALSE) 
mod8 <- lme4::lmer(fitness ~ Generation + (1|Generation:Fruit_s), 
            weights = N, data = data_sum[data_sum$Phase=="second_postpool",], REML = FALSE) 


anova(mod1,mod2)
## Data: data_sum[data_sum$Phase == "second_postpool", ]
## Models:
## mod1: fitness ~ 1 + (1 | Generation:Fruit_s)
## mod2: fitness ~ Line + (1 | Generation:Fruit_s)
##      npar    AIC    BIC  logLik deviance  Chisq Df Pr(>Chisq)  
## mod1    3 183.90 193.41 -88.949   177.90                       
## mod2   13 180.93 222.15 -77.466   154.93 22.966 10    0.01087 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
MuMIn::model.sel(mod1,mod2,mod3,mod4,mod5,mod6, mod7, mod8)
## Model selection table 
##       (Int) Lin Frt_s       Gnr Gnr:Lin Frt_s:Gnr             family df  logLik
## mod3 0.6953         +                             gaussian(identity)  5 -85.570
## mod7 0.8959         + -0.010180                   gaussian(identity)  6 -85.075
## mod2 0.6794   +                                   gaussian(identity) 13 -77.466
## mod1 0.5241                                       gaussian(identity)  3 -88.949
## mod5 0.8956   +       -0.010990                   gaussian(identity) 14 -76.903
## mod8 0.7116           -0.009572                   gaussian(identity)  4 -88.569
## mod6 0.7081         + -0.000622                 + gaussian(identity)  8 -84.555
## mod4 0.9618   +       -0.014300       +           gaussian(identity) 24 -67.423
##       AICc delta weight
## mod3 181.5  0.00  0.362
## mod7 182.6  1.15  0.203
## mod2 183.2  1.69  0.156
## mod1 184.0  2.54  0.101
## mod5 184.4  2.92  0.084
## mod8 185.4  3.88  0.052
## mod6 186.0  4.48  0.039
## mod4 190.8  9.30  0.003
## Models ranked by AICc(x) 
## Random terms (all models): 
## '1 | Generation:Fruit_s'
mod3 <- lme4::lmer(fitness ~ Fruit_s-1 + (1|Generation) + (1|Generation:Fruit_s), 
            weights = N, data = data_sum[data_sum$Phase=="second_postpool",]) 

summary(mod3)
## Linear mixed model fit by REML ['lmerMod']
## Formula: fitness ~ Fruit_s - 1 + (1 | Generation) + (1 | Generation:Fruit_s)
##    Data: data_sum[data_sum$Phase == "second_postpool", ]
## Weights: N
## 
## REML criterion at convergence: 179
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.9294 -0.2956  0.0853  0.4437  2.0606 
## 
## Random effects:
##  Groups             Name        Variance Std.Dev.
##  Generation:Fruit_s (Intercept) 0.05947  0.2439  
##  Generation         (Intercept) 0.02245  0.1498  
##  Residual                       2.44241  1.5628  
## Number of obs: 176, groups:  Generation:Fruit_s, 48; Generation, 16
## 
## Fixed effects:
##                   Estimate Std. Error t value
## Fruit_sCherry      0.69438    0.08542   8.129
## Fruit_sCranberry   0.49602    0.08074   6.143
## Fruit_sStrawberry  0.38278    0.08553   4.475
## 
## Correlation of Fixed Effects:
##             Frt_sCh Frt_sCr
## Frt_sCrnbrr 0.204          
## Frt_sStrwbr 0.192   0.204

6 SUP: Correlated responses for other traits

6.1 Formating data

#Formatting for fecundity
TEMP_dataG7_CheCran_Fecundity <- formattinglogchange(logchange_dataset = data_logchange, generation="7", 
                                           fruitcomb=c("Cherry", "Cranberry"), trait="fecundity")
TEMP_dataG7_CranStraw_Fecundity <- formattinglogchange(logchange_dataset = data_logchange, generation="7", 
                                             fruitcomb=c("Strawberry", "Cranberry"), trait="fecundity")
TEMP_dataG7_StrawChe_Fecundity <- formattinglogchange(logchange_dataset = data_logchange, generation="7", 
                                            fruitcomb=c("Cherry", "Strawberry"), trait="fecundity")

TEMP_dataG29_CheCran_Fecundity <- formattinglogchange(logchange_dataset = data_logchange, generation="29", 
                                            fruitcomb=c("Cherry", "Cranberry"), trait="fecundity")
TEMP_dataG29_CranStraw_Fecundity <- formattinglogchange(logchange_dataset = data_logchange, generation="29", 
                                              fruitcomb=c("Strawberry", "Cranberry"), trait="fecundity")
TEMP_dataG29_StrawChe_Fecundity <- formattinglogchange(logchange_dataset = data_logchange, generation="29", 
                                             fruitcomb=c("Cherry", "Strawberry"), trait="fecundity")



#Formatting for egg to adult viability
TEMP_dataG7_CheCran_Viability <- formattinglogchange(logchange_dataset = data_logchange, generation="7", 
                                           fruitcomb=c("Cherry", "Cranberry"), trait="eggtoad")
TEMP_dataG7_CranStraw_Viability  <- formattinglogchange(logchange_dataset = data_logchange, generation="7", 
                                             fruitcomb=c("Strawberry", "Cranberry"), trait="eggtoad")
TEMP_dataG7_StrawChe_Viability  <- formattinglogchange(logchange_dataset = data_logchange, generation="7", 
                                            fruitcomb=c("Cherry", "Strawberry"), trait="eggtoad")

TEMP_dataG29_CheCran_Viability  <- formattinglogchange(logchange_dataset = data_logchange, generation="29", 
                                            fruitcomb=c("Cherry", "Cranberry"), trait="eggtoad")
TEMP_dataG29_CranStraw_Viability  <- formattinglogchange(logchange_dataset = data_logchange, generation="29", 
                                              fruitcomb=c("Strawberry", "Cranberry"), trait="eggtoad")
TEMP_dataG29_StrawChe_Viability  <- formattinglogchange(logchange_dataset = data_logchange, generation="29", 
                                             fruitcomb=c("Cherry", "Strawberry"), trait="eggtoad")

6.2 Plot Fecundity

#Formating data
ymin = -50
ymax = 50

##################################################
##################   G7
# Limits
ymin_CheCranG7=min(min(TEMP_dataG7_CheCran_Fecundity$lowCIlogfecundchange_allop, na.rm= TRUE),
                min(TEMP_dataG7_CheCran_Fecundity$lowCIlogfecundchange_symp, na.rm= TRUE)) 
ymax_CheCranG7=max(max(TEMP_dataG7_CheCran_Fecundity$upCIlogfecundchange_allop, na.rm= TRUE),
                max(TEMP_dataG7_CheCran_Fecundity$upCIlogfecundchange_symp, na.rm= TRUE))
lim_text<-2



# Plot
CheCran_G7 <- ggplot(data = TEMP_dataG7_CheCran_Fecundity) + 
  geom_errorbar(aes(x = logfecundchange_symp, ymin = lowCIlogfecundchange_allop,
                    ymax = upCIlogfecundchange_allop,
                    color = Fruit_s),
                width= 0.02, size = 0.3, alpha = 0.8) + 
  geom_errorbarh(aes(y = logfecundchange_allop, xmin = lowCIlogfecundchange_symp, 
                     xmax = upCIlogfecundchange_symp, 
                     color = Fruit_s),
                 height = 0.02, size = 0.3, alpha = 0.8) + 
  geom_point(aes(x = logfecundchange_symp, y = logfecundchange_allop,  
                 color = Fruit_s,fill = Fruit_s, shape = Treatment),
             size = 3, fill = "white", stroke =1.2) + 
  xlab("Log fecundity change in\nselective environment")  + 
  ylab("Log fecundity change in\nalternative environment")  + 
  ggtitle("Cherry vs. Cranberry") + 
  theme_LO_sober + 
  labs(shape = "Test fruit", color = "Evolution fruit") + 
  scale_shape_manual(values = c(21, 22)) + 
  scale_color_manual(values = c("#BC3C6D", "#FDB424"))  + 
  coord_cartesian(ylim = c(ymin_CheCranG7, ymax_CheCranG7), 
                  xlim = c(ymin_CheCranG7, ymax_CheCranG7)) 
  
 CheCran_G7

# Limits
ymin_CranStrawG7=min(min(TEMP_dataG7_CranStraw_Fecundity$lowCIlogfecundchange_allop, na.rm= TRUE),
                min(TEMP_dataG7_CranStraw_Fecundity$lowCIlogfecundchange_symp, na.rm= TRUE))
ymax_CranStrawG7=max(max(TEMP_dataG7_CranStraw_Fecundity$upCIlogfecundchange_allop, na.rm= TRUE),
                max(TEMP_dataG7_CranStraw_Fecundity$upCIlogfecundchange_symp, na.rm= TRUE))
lim_text<-ymin_CranStrawG7+0.99*(ymax_CranStrawG7-ymin_CranStrawG7)


CranStraw_G7 <-  ggplot(data = TEMP_dataG7_CranStraw_Fecundity) + 
  geom_errorbar(aes(x = logfecundchange_symp, ymin = lowCIlogfecundchange_allop, 
                    ymax = upCIlogfecundchange_allop,
                    color = Fruit_s),
                  width= 0.02, size = 0.3, alpha = 0.8) + 
  geom_errorbarh(aes(y = logfecundchange_allop, xmin = lowCIlogfecundchange_symp, 
                     xmax = upCIlogfecundchange_allop, 
                 color = Fruit_s),
                 height = 0.02, size = 0.3, alpha = 0.8) + 
  geom_point(aes(x =logfecundchange_symp, y = logfecundchange_allop,  color = Fruit_s,
                 fill = Fruit_s, shape = Treatment),
                   size =3, fill = "white", stroke =1.2) + 
  xlab("Log fecundity change in\nselective environment")  + 
  ylab("Log fecundity change in\nalternative environment")  + 
     ggtitle("Cranberry vs. Strawberry") + 
  coord_cartesian(ylim = c(ymin_CranStrawG7, ymax_CranStrawG7), 
                  xlim = c(ymin_CranStrawG7, ymax_CranStrawG7)) + 
   labs(shape = "Test fruit", color = "Selection fruit") + 
   scale_shape_manual(values = c(22, 24)) + 
   scale_color_manual(values = c("#FDB424", "#3FAA96"))  + 
  theme_LO_sober
 CranStraw_G7

# Limits
ymin_StrawCheG7=min(min(TEMP_dataG7_StrawChe_Fecundity$lowCIlogfecundchange_allop, na.rm= TRUE),
                min(TEMP_dataG7_StrawChe_Fecundity$lowCIlogfecundchange_symp, na.rm= TRUE))
ymax_StrawCheG7=max(max(TEMP_dataG7_StrawChe_Fecundity$upCIlogfecundchange_allop, na.rm= TRUE),
                max(TEMP_dataG7_StrawChe_Fecundity$lowCIlogfecundchange_symp, na.rm= TRUE))
lim_text<-ymin_StrawCheG7+0.99*(ymax_StrawCheG7-ymin_StrawCheG7)


StrawChe_G7 <-  ggplot(data = TEMP_dataG7_StrawChe_Fecundity) + 
  geom_errorbar(aes(x =logfecundchange_symp, 
                    ymin = lowCIlogfecundchange_allop, 
                    ymax = upCIlogfecundchange_allop,
                    color = Fruit_s),
                  width= 0.02, size = 0.3, alpha = 0.8) + 
  geom_errorbarh(aes(y = logfecundchange_allop, 
                     xmin = lowCIlogfecundchange_symp, 
                     xmax = upCIlogfecundchange_symp, 
                 color = Fruit_s),
                 height = 0.02, size = 0.3, alpha = 0.8) + 
  geom_point(aes(x = logfecundchange_symp, y = logfecundchange_allop,  color = Fruit_s,fill = Fruit_s, shape = Treatment),
                 size =3, fill = "white", stroke =1.2) + 
  xlab("Log fecundity change in\nselective environment")  + 
  ylab("Log fecundity change in\nalternative environment")  + 
  ggtitle("Strawberry vs. Cherry") + 
  labs(shape = "Test fruit", color = "Selection fruit") + 
  scale_shape_manual(values = c(21, 24)) + 
  scale_color_manual(values = c("#BC3C6D", "#3FAA96"))  + 
  coord_cartesian(ylim = c(ymin_StrawCheG7, ymax_StrawCheG7), 
                  xlim = c(ymin_StrawCheG7, ymax_StrawCheG7)) + 
  theme_LO_sober
 StrawChe_G7

#####################################################################################
##################################      G29        ##################################
#####################################################################################
TEMP_dataG29_CheCran
##    Treatment Line   Fruit_s Generation N_symp logchange_symp
## 5     Cherry  CEA    Cherry         29     30     0.14767000
## 6     Cherry  CEB    Cherry         29     30     0.25038029
## 7     Cherry  CEC    Cherry         29     30     0.35854062
## 38 Cranberry  CRA Cranberry         29     30    -0.14885350
## 39 Cranberry  CRB Cranberry         29     30     0.29719689
## 40 Cranberry  CRC Cranberry         29     30     0.02703717
## 41 Cranberry  CRD Cranberry         29     30    -0.15627744
## 42 Cranberry  CRE Cranberry         29     30     0.72513486
##    lowCIlogfitnesschange_symp upCIlogfitnesschange_symp N_allop logchange_allop
## 5                 -0.12317382                 0.4301216      30     -0.21774071
## 6                 -0.01964191                 0.5320919      30     -0.59200204
## 7                  0.08929875                 0.6395485      30     -0.20670829
## 38                -0.42196368                 0.1356310      30      0.32537794
## 39                 0.02797352                 0.5781852      30      0.16929862
## 40                -0.24433581                 0.3099615      30      0.08474123
## 41                -0.42946769                 0.1282789      30      0.54736475
## 42                 0.45833897                 1.0039294      30     -0.10496044
##    lowCIlogfitnesschange_allop upCIlogfitnesschange_allop N_sumsympallop
## 5                  -0.49161649                 0.06743017             60
## 6                  -0.87105791                -0.30220654             60
## 7                  -0.48045799                 0.07834961             60
## 38                  0.05590556                 0.60659377             60
## 39                 -0.10136525                 0.45158825             60
## 40                 -0.18664831                 0.36768394             60
## 41                  0.27930142                 0.82730827             60
## 42                 -0.37821315                 0.17965592             60
TEMP_dataG29_CranStraw
##     Treatment Line    Fruit_s Generation N_symp logchange_symp
## 38  Cranberry  CRA  Cranberry         29     30    -0.14885350
## 39  Cranberry  CRB  Cranberry         29     30     0.29719689
## 40  Cranberry  CRC  Cranberry         29     30     0.02703717
## 41  Cranberry  CRD  Cranberry         29     30    -0.15627744
## 42  Cranberry  CRE  Cranberry         29     30     0.72513486
## 73 Strawberry  FRA Strawberry         29     30     0.15709032
## 74 Strawberry  FRB Strawberry         29     30     0.56023100
## 75 Strawberry  FRC Strawberry         29     30     0.36640738
##    lowCIlogfitnesschange_symp upCIlogfitnesschange_symp N_allop logchange_allop
## 38                -0.42196368                 0.1356310      30     -0.29731422
## 39                 0.02797352                 0.5781852      30     -0.24539416
## 40                -0.24433581                 0.3099615      30     -0.28950778
## 41                -0.42946769                 0.1282789      30     -1.02555272
## 42                 0.45833897                 1.0039294      30     -0.16810249
## 73                -0.11224578                 0.4381748      30     -0.25158762
## 74                 0.29332571                 0.8391204      30      0.04303751
## 75                 0.09845305                 0.6462450      30      0.52109773
##    lowCIlogfitnesschange_allop upCIlogfitnesschange_allop N_sumsympallop
## 38                  -0.5708280               -0.012475350             60
## 39                  -0.5183319                0.038928334             60
## 40                  -0.5629331               -0.004748193             60
## 41                  -1.3110679               -0.730048996             60
## 42                  -0.4402347                0.115497271             60
## 73                  -0.5258587                0.033937386             60
## 74                  -0.2281917                0.325832559             60
## 75                   0.2532712                0.800824675             60
TEMP_dataG29_StrawChe
##     Treatment Line    Fruit_s Generation N_symp logchange_symp
## 5      Cherry  CEA     Cherry         29     30      0.1476700
## 6      Cherry  CEB     Cherry         29     30      0.2503803
## 7      Cherry  CEC     Cherry         29     30      0.3585406
## 73 Strawberry  FRA Strawberry         29     30      0.1570903
## 74 Strawberry  FRB Strawberry         29     30      0.5602310
## 75 Strawberry  FRC Strawberry         29     30      0.3664074
##    lowCIlogfitnesschange_symp upCIlogfitnesschange_symp N_allop logchange_allop
## 5                 -0.12317382                 0.4301216      30      0.01971359
## 6                 -0.01964191                 0.5320919      30     -0.15850680
## 7                  0.08929875                 0.6395485      30      0.21131464
## 73                -0.11224578                 0.4381748      30     -0.50599095
## 74                 0.29332571                 0.8391204      30      0.19377147
## 75                 0.09845305                 0.6462450      30      0.32151693
##    lowCIlogfitnesschange_allop upCIlogfitnesschange_allop N_sumsympallop
## 5                  -0.25069640                  0.3017653             60
## 6                  -0.43054314                  0.1250069             60
## 7                  -0.05763581                  0.4920514             60
## 73                 -0.78451399                 -0.2166643             60
## 74                 -0.07669333                  0.4758818             60
## 75                  0.05201722                  0.6027574             60
# Limits
ymin_CheCranG29=min(min(TEMP_dataG29_CheCran_Fecundity$lowCIlogfecundchange_allop, na.rm= TRUE),
                min(TEMP_dataG29_CheCran_Fecundity$lowCIlogfecundchange_symp, na.rm= TRUE))
ymax_CheCranG29=max(max(TEMP_dataG29_CheCran_Fecundity$upCIlogfecundchange_allop, na.rm= TRUE),
                max(TEMP_dataG29_CheCran_Fecundity$upCIlogfecundchange_symp, na.rm= TRUE))
lim_text<-ymin_CheCranG29+0.99*(ymax_CheCranG29-ymin_CheCranG29)


CheCran_G29 <- ggplot(data = TEMP_dataG29_CheCran_Fecundity) + 
  geom_errorbar(aes(x =logfecundchange_symp, ymin = lowCIlogfecundchange_allop, 
                    ymax = upCIlogfecundchange_allop,
                    color = Fruit_s),
                  width= 0.02, size = 0.3, alpha = 0.8) + 
  geom_errorbarh(aes(y = logfecundchange_allop, xmin = lowCIlogfecundchange_symp, 
                     xmax = upCIlogfecundchange_symp, 
                 color = Fruit_s),
                 height = 0.02, size = 0.3, alpha = 0.8) + 
  geom_point(aes(x =logfecundchange_symp, y = logfecundchange_allop,  color = Fruit_s, fill = Fruit_s, shape = Treatment),
                 size =3, fill = "white", stroke =1.2) + 
  xlab("Log fecundity change in\nselective environment")  + 
  ylab("Log fecundity change in\nalternative environment")  + 
  ggtitle("Cherry vs. Cranberry") + 
  labs(shape = "Test fruit", color = "Evolution fruit") + 
  scale_shape_manual(values = c(16, 15)) + 
  scale_color_manual(values = c("#BC3C6D", "#FDB424"))  + 
  coord_cartesian(ylim = c(ymin_CheCranG29, ymax_CheCranG29), 
                  xlim = c(ymin_CheCranG29, ymax_CheCranG29)) + 
  theme_LO_sober
 CheCran_G29

# Limits
ymin_CranStrawG29=min(min(TEMP_dataG29_CranStraw_Fecundity$lowCIlogfecundchange_allop, na.rm= TRUE),
                min(TEMP_dataG29_CranStraw_Fecundity$lowCIlogfecundchange_symp, na.rm= TRUE))
ymax_CranStrawG29=max(max(TEMP_dataG29_CranStraw_Fecundity$upCIlogfecundchange_allop, na.rm= TRUE),
                max(TEMP_dataG29_CranStraw_Fecundity$upCIlogfecundchange_symp, na.rm= TRUE))
lim_text<-ymin_CranStrawG29+0.99*(ymax_CranStrawG29-ymin_CranStrawG29)

#Plot
 CranStraw_G29 <- ggplot(data = TEMP_dataG29_CranStraw_Fecundity) + 
   geom_errorbar(aes(x =logfecundchange_symp, ymin = lowCIlogfecundchange_allop, 
                    ymax = upCIlogfecundchange_allop,
                    color = Fruit_s),
                  width= 0.02, size = 0.3, alpha = 0.8) + 
   geom_errorbarh(aes(y = logfecundchange_allop, xmin = lowCIlogfecundchange_symp,
                      xmax = upCIlogfecundchange_symp, 
                 color = Fruit_s),
                 height = 0.02, size = 0.3, alpha = 0.8) + 
   geom_point(aes(x =logfecundchange_symp, y = logfecundchange_allop,  
                  color = Fruit_s,fill = Fruit_s, shape = Treatment),
                 size =3, fill = "white", stroke =1.2) + 
   xlab("Log fecundity change in\nselective environment")  + 
   ylab("Log fecundity change in\nalternative environment")  + 
   ggtitle("Cranberry vs. Strawberry") + 
   coord_cartesian(ylim = c(ymin_CranStrawG29, ymax_CranStrawG29), 
                  xlim = c(ymin_CranStrawG29, ymax_CranStrawG29)) + 
   labs(shape = "Test fruit", color = "Selection fruit") + 
   scale_shape_manual(values = c(15, 17)) + 
   scale_color_manual(values = c("#FDB424", "#3FAA96"))  + 
   theme_LO_sober
 CranStraw_G29

# Limits
ymin_StrawCheG29=min(min(TEMP_dataG29_StrawChe_Fecundity$lowCIlogfecundchange_allop, na.rm= TRUE),
                min(TEMP_dataG29_StrawChe_Fecundity$lowCIlogfecundchange_symp, na.rm= TRUE))
ymax_StrawCheG29=max(max(TEMP_dataG29_StrawChe_Fecundity$upCIlogfecundchange_allop, na.rm= TRUE),
                max(TEMP_dataG29_StrawChe_Fecundity$upCIlogfecundchange_symp, na.rm= TRUE))
lim_text<-ymin_StrawCheG29+0.99*(ymax_StrawCheG29-ymin_StrawCheG29)


StrawChe_G29 <- ggplot(data = TEMP_dataG29_StrawChe_Fecundity) + 
  geom_errorbar(aes(x =logfecundchange_symp, ymin = lowCIlogfecundchange_allop, 
                    ymax = upCIlogfecundchange_allop,
                    color = Fruit_s),
                  width= 0.02, size = 0.2, alpha =1) + 
  geom_errorbarh(aes(y = logfecundchange_allop, xmin = lowCIlogfecundchange_symp, 
                     xmax = upCIlogfecundchange_symp, 
                 color = Fruit_s),
                 height = 0.02, size = 0.2, alpha =1) + 
  geom_point(aes(x =logfecundchange_symp, y = logfecundchange_allop,  
                 color = Fruit_s, fill = Fruit_s, shape = Treatment),
                 size =3, fill = "white", stroke =1.2) + 
  coord_cartesian(ylim = c(ymin_StrawCheG29, ymax_StrawCheG29), 
                  xlim = c(ymin_StrawCheG29, ymax_StrawCheG29)) + 
    xlab("Log fecundity change in\nselective environment")  + 
    ylab("Log fecundity change in\nalternative environment")  + 
  ggtitle("Strawberry vs. Cherry") + 
  labs(shape = "Test fruit", color = "Selection fruit") + 
  scale_shape_manual(values = c(16, 17)) + 
  scale_color_manual(values = c("#BC3C6D", "#3FAA96"))  + 
  theme_LO_sober
 StrawChe_G29

legend_tradevide <-  ggplot(data = data_logchange[data_logchange$Generation == "29",],
                          aes(x =logchange, y = logchange,  color = Fruit_s, fill = Fruit_s, 
                              shape = Treatment)) + 
  geom_point(size =2.5, fill = "white") + 
  labs(shape = "Test fruit", color = "Selection fruit") + 
  scale_shape_manual(values = c(16, 15, 17)) + 
  scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96"))  + 
  theme_LO_sober

legend_trade <- lemon::g_legend(legend_tradevide)

 
 

Slopeestimates_logchange_pairwise_errorbar_FECUNDITY <- cowplot::ggdraw() + 
  cowplot::draw_plot(CheCran_G7 + theme(legend.position = "none"), 
            x = 0.01, y = 0.5, width = 0.22, height = 0.45) + 
  cowplot::draw_plot(CranStraw_G7 + theme(legend.position = "none"), 
            x = 0.31, y = 0.5, width = 0.22, height = 0.45) + 
  cowplot::draw_plot(StrawChe_G7 + theme(legend.position = "none"), 
            x = 0.61, y = 0.5, width = 0.22, height = 0.45) + 
  cowplot::draw_plot(legend_trade, x = 0.85, y = 0.5, width = 0.1, height = 0.1) + 
  cowplot::draw_plot(CheCran_G29 + theme(legend.position = "none"), 
            x = 0.01, y = 0, width = 0.22, height = 0.45) + 
  cowplot::draw_plot(CranStraw_G29 + theme(legend.position = "none"), 
            x = 0.31, y = 0, width = 0.22, height = 0.45) + 
  cowplot::draw_plot(StrawChe_G29 + theme(legend.position = "none"), 
            x = 0.61, y = 0, width = 0.22, height = 0.45) + 
  cowplot::draw_plot_label(c("Intermediate phenotyping step", "A", "B", "C", " ",
                    "Final phenotyping step", "D", "E", "F", " "),  
                  x = c(0.30, 0.01, 0.30, 0.61, 0.92, 0.250, 0.01, 0.30, 0.61, 0.92), 
                  y = c(0.99, 0.95, 0.95, 0.95, 0.95, 0.49, 0.45, 0.45, 0.45, 0.45), 
                  hjust = c(-0.25, -0.25, -0.25, -0.25, -0.25, -0.75, -0.75, -0.75, -0.75, -0.75), 
                  vjust = c(1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5),
                  size = 16) 
 Slopeestimates_logchange_pairwise_errorbar_FECUNDITY

#  
# #  
# 
cowplot::save_plot(file =here::here("figures", "SUPMAT_Correlation_Fecundity.pdf"),
                   Slopeestimates_logchange_pairwise_errorbar_FECUNDITY,
                   base_height = 18/cm(1), base_width = 34/cm(1), dpi = 610)

6.3 Plot Egg to adult viability

#Formating data
ymin = -50
ymax = 50

##################################################
##################   G7
# Limits
ymin_CheCranG7=min(min(TEMP_dataG7_CheCran_Viability$lowCIlogeggtoadchange_allop, na.rm= TRUE),
                min(TEMP_dataG7_CheCran_Viability$lowCIlogeggtoadchange_symp, na.rm= TRUE)) 
ymax_CheCranG7=max(max(TEMP_dataG7_CheCran_Viability$upCIlogeggtoadchange_allop, na.rm= TRUE),
                max(TEMP_dataG7_CheCran_Viability$upCIlogeggtoadchange_symp, na.rm= TRUE))
lim_text<-2



# Plot
CheCran_G7 <- ggplot(data = TEMP_dataG7_CheCran_Viability) + 
  geom_errorbar(aes(x = logeggtoadchange_symp, ymin = lowCIlogeggtoadchange_allop,
                    ymax = upCIlogeggtoadchange_allop,
                    color = Fruit_s),
                width= 0.02, size = 0.3, alpha = 0.8) + 
  geom_errorbarh(aes(y = logeggtoadchange_allop, xmin = lowCIlogeggtoadchange_symp, 
                     xmax = upCIlogeggtoadchange_symp, 
                     color = Fruit_s),
                 height = 0.02, size = 0.3, alpha = 0.8) + 
  geom_point(aes(x = logeggtoadchange_symp, y = logeggtoadchange_allop,  
                 color = Fruit_s,fill = Fruit_s, shape = Treatment),
             size = 3, fill = "white", stroke =1.2) + 
  xlab("Logit egg-to-adult viability\nchange in selective environment")  + 
  ylab("Logit egg-to-adult viability\nchange in alternative environment")  + 
  ggtitle("Cherry vs. Cranberry") + 
  theme_LO_sober + 
  labs(shape = "Test fruit", color = "Evolution fruit") + 
  scale_shape_manual(values = c(21, 22)) + 
  scale_color_manual(values = c("#BC3C6D", "#FDB424"))  + 
  coord_cartesian(ylim = c(ymin_CheCranG7, ymax_CheCranG7), 
                  xlim = c(ymin_CheCranG7, ymax_CheCranG7)) 
  
 CheCran_G7

# Limits
ymin_CranStrawG7=min(min(TEMP_dataG7_CranStraw_Viability$lowCIlogeggtoadchange_allop, na.rm= TRUE),
                min(TEMP_dataG7_CranStraw_Viability$lowCIlogeggtoadchange_symp, na.rm= TRUE))
ymax_CranStrawG7=max(max(TEMP_dataG7_CranStraw_Viability$upCIlogeggtoadchange_allop, na.rm= TRUE),
                max(TEMP_dataG7_CranStraw_Viability$upCIlogeggtoadchange_symp, na.rm= TRUE))
lim_text<-ymin_CranStrawG7+0.99*(ymax_CranStrawG7-ymin_CranStrawG7)


CranStraw_G7 <-  ggplot(data = TEMP_dataG7_CranStraw_Viability) + 
  geom_errorbar(aes(x = logeggtoadchange_symp, ymin = lowCIlogeggtoadchange_allop, 
                    ymax = upCIlogeggtoadchange_allop,
                    color = Fruit_s),
                  width= 0.02, size = 0.3, alpha = 0.8) + 
  geom_errorbarh(aes(y = logeggtoadchange_allop, xmin = lowCIlogeggtoadchange_symp, 
                     xmax = upCIlogeggtoadchange_allop, 
                 color = Fruit_s),
                 height = 0.02, size = 0.3, alpha = 0.8) + 
  geom_point(aes(x =logeggtoadchange_symp, y = logeggtoadchange_allop,  color = Fruit_s,
                 fill = Fruit_s, shape = Treatment),
                   size =3, fill = "white", stroke =1.2) + 
  xlab("Logit egg-to-adult viability\nchange in selective environment")  + 
  ylab("Logit egg-to-adult viability\nchange in alternative environment")  +  
     ggtitle("Cranberry vs. Strawberry") + 
  coord_cartesian(ylim = c(ymin_CranStrawG7, ymax_CranStrawG7), 
                  xlim = c(ymin_CranStrawG7, ymax_CranStrawG7)) + 
   labs(shape = "Test fruit", color = "Selection fruit") + 
   scale_shape_manual(values = c(22, 24)) + 
   scale_color_manual(values = c("#FDB424", "#3FAA96"))  + 
  theme_LO_sober
 CranStraw_G7

# Limits
ymin_StrawCheG7=min(min(TEMP_dataG7_StrawChe_Viability$lowCIlogeggtoadchange_allop, na.rm= TRUE),
                min(TEMP_dataG7_StrawChe_Viability$lowCIlogeggtoadchange_symp, na.rm= TRUE))
ymax_StrawCheG7=max(max(TEMP_dataG7_StrawChe_Viability$upCIlogeggtoadchange_allop, na.rm= TRUE),
                max(TEMP_dataG7_StrawChe_Viability$lowCIlogeggtoadchange_symp, na.rm= TRUE))
lim_text<-ymin_StrawCheG7+0.99*(ymax_StrawCheG7-ymin_StrawCheG7)


StrawChe_G7 <-  ggplot(data = TEMP_dataG7_StrawChe_Viability) + 
  geom_errorbar(aes(x =logeggtoadchange_symp, 
                    ymin = lowCIlogeggtoadchange_allop, 
                    ymax = upCIlogeggtoadchange_allop,
                    color = Fruit_s),
                  width= 0.02, size = 0.3, alpha = 0.8) + 
  geom_errorbarh(aes(y = logeggtoadchange_allop, 
                     xmin = lowCIlogeggtoadchange_symp, 
                     xmax = upCIlogeggtoadchange_symp, 
                 color = Fruit_s),
                 height = 0.02, size = 0.3, alpha = 0.8) + 
  geom_point(aes(x = logeggtoadchange_symp, y = logeggtoadchange_allop,  
                 color = Fruit_s, fill = Fruit_s, shape = Treatment),
                 size =3, fill = "white", stroke =1.2) + 
  xlab("Logit egg-to-adult viability\nchange in selective environment")  + 
  ylab("Logit egg-to-adult viability\nchange in alternative environment")  + 
  ggtitle("Strawberry vs. Cherry") + 
  labs(shape = "Test fruit", color = "Selection fruit") + 
  scale_shape_manual(values = c(21, 24)) + 
  scale_color_manual(values = c("#BC3C6D", "#3FAA96"))  + 
  coord_cartesian(ylim = c(ymin_StrawCheG7, ymax_StrawCheG7), 
                  xlim = c(ymin_StrawCheG7, ymax_StrawCheG7)) + 
  theme_LO_sober
 StrawChe_G7

#####################################################################################
##################################      G29        ##################################
#####################################################################################
TEMP_dataG29_CheCran
##    Treatment Line   Fruit_s Generation N_symp logchange_symp
## 5     Cherry  CEA    Cherry         29     30     0.14767000
## 6     Cherry  CEB    Cherry         29     30     0.25038029
## 7     Cherry  CEC    Cherry         29     30     0.35854062
## 38 Cranberry  CRA Cranberry         29     30    -0.14885350
## 39 Cranberry  CRB Cranberry         29     30     0.29719689
## 40 Cranberry  CRC Cranberry         29     30     0.02703717
## 41 Cranberry  CRD Cranberry         29     30    -0.15627744
## 42 Cranberry  CRE Cranberry         29     30     0.72513486
##    lowCIlogfitnesschange_symp upCIlogfitnesschange_symp N_allop logchange_allop
## 5                 -0.12317382                 0.4301216      30     -0.21774071
## 6                 -0.01964191                 0.5320919      30     -0.59200204
## 7                  0.08929875                 0.6395485      30     -0.20670829
## 38                -0.42196368                 0.1356310      30      0.32537794
## 39                 0.02797352                 0.5781852      30      0.16929862
## 40                -0.24433581                 0.3099615      30      0.08474123
## 41                -0.42946769                 0.1282789      30      0.54736475
## 42                 0.45833897                 1.0039294      30     -0.10496044
##    lowCIlogfitnesschange_allop upCIlogfitnesschange_allop N_sumsympallop
## 5                  -0.49161649                 0.06743017             60
## 6                  -0.87105791                -0.30220654             60
## 7                  -0.48045799                 0.07834961             60
## 38                  0.05590556                 0.60659377             60
## 39                 -0.10136525                 0.45158825             60
## 40                 -0.18664831                 0.36768394             60
## 41                  0.27930142                 0.82730827             60
## 42                 -0.37821315                 0.17965592             60
TEMP_dataG29_CranStraw
##     Treatment Line    Fruit_s Generation N_symp logchange_symp
## 38  Cranberry  CRA  Cranberry         29     30    -0.14885350
## 39  Cranberry  CRB  Cranberry         29     30     0.29719689
## 40  Cranberry  CRC  Cranberry         29     30     0.02703717
## 41  Cranberry  CRD  Cranberry         29     30    -0.15627744
## 42  Cranberry  CRE  Cranberry         29     30     0.72513486
## 73 Strawberry  FRA Strawberry         29     30     0.15709032
## 74 Strawberry  FRB Strawberry         29     30     0.56023100
## 75 Strawberry  FRC Strawberry         29     30     0.36640738
##    lowCIlogfitnesschange_symp upCIlogfitnesschange_symp N_allop logchange_allop
## 38                -0.42196368                 0.1356310      30     -0.29731422
## 39                 0.02797352                 0.5781852      30     -0.24539416
## 40                -0.24433581                 0.3099615      30     -0.28950778
## 41                -0.42946769                 0.1282789      30     -1.02555272
## 42                 0.45833897                 1.0039294      30     -0.16810249
## 73                -0.11224578                 0.4381748      30     -0.25158762
## 74                 0.29332571                 0.8391204      30      0.04303751
## 75                 0.09845305                 0.6462450      30      0.52109773
##    lowCIlogfitnesschange_allop upCIlogfitnesschange_allop N_sumsympallop
## 38                  -0.5708280               -0.012475350             60
## 39                  -0.5183319                0.038928334             60
## 40                  -0.5629331               -0.004748193             60
## 41                  -1.3110679               -0.730048996             60
## 42                  -0.4402347                0.115497271             60
## 73                  -0.5258587                0.033937386             60
## 74                  -0.2281917                0.325832559             60
## 75                   0.2532712                0.800824675             60
TEMP_dataG29_StrawChe
##     Treatment Line    Fruit_s Generation N_symp logchange_symp
## 5      Cherry  CEA     Cherry         29     30      0.1476700
## 6      Cherry  CEB     Cherry         29     30      0.2503803
## 7      Cherry  CEC     Cherry         29     30      0.3585406
## 73 Strawberry  FRA Strawberry         29     30      0.1570903
## 74 Strawberry  FRB Strawberry         29     30      0.5602310
## 75 Strawberry  FRC Strawberry         29     30      0.3664074
##    lowCIlogfitnesschange_symp upCIlogfitnesschange_symp N_allop logchange_allop
## 5                 -0.12317382                 0.4301216      30      0.01971359
## 6                 -0.01964191                 0.5320919      30     -0.15850680
## 7                  0.08929875                 0.6395485      30      0.21131464
## 73                -0.11224578                 0.4381748      30     -0.50599095
## 74                 0.29332571                 0.8391204      30      0.19377147
## 75                 0.09845305                 0.6462450      30      0.32151693
##    lowCIlogfitnesschange_allop upCIlogfitnesschange_allop N_sumsympallop
## 5                  -0.25069640                  0.3017653             60
## 6                  -0.43054314                  0.1250069             60
## 7                  -0.05763581                  0.4920514             60
## 73                 -0.78451399                 -0.2166643             60
## 74                 -0.07669333                  0.4758818             60
## 75                  0.05201722                  0.6027574             60
# Limits
ymin_CheCranG29=min(min(TEMP_dataG29_CheCran_Viability$lowCIlogeggtoadchange_allop, na.rm= TRUE),
                min(TEMP_dataG29_CheCran_Viability$lowCIlogeggtoadchange_symp, na.rm= TRUE))
ymax_CheCranG29=max(max(TEMP_dataG29_CheCran_Viability$upCIlogeggtoadchange_allop, na.rm= TRUE),
                max(TEMP_dataG29_CheCran_Viability$upCIlogeggtoadchange_symp, na.rm= TRUE))
lim_text<-ymin_CheCranG29+0.99*(ymax_CheCranG29-ymin_CheCranG29)


CheCran_G29 <- ggplot(data = TEMP_dataG29_CheCran_Viability) + 
  geom_errorbar(aes(x =logeggtoadchange_symp, ymin = lowCIlogeggtoadchange_allop, 
                    ymax = upCIlogeggtoadchange_allop,
                    color = Fruit_s),
                  width= 0.02, size = 0.3, alpha = 0.8) + 
  geom_errorbarh(aes(y = logeggtoadchange_allop, xmin = lowCIlogeggtoadchange_symp, 
                     xmax = upCIlogeggtoadchange_symp, 
                 color = Fruit_s),
                 height = 0.02, size = 0.3, alpha = 0.8) + 
  geom_point(aes(x =logeggtoadchange_symp, y = logeggtoadchange_allop,  color = Fruit_s, fill = Fruit_s, shape = Treatment),
                 size =3, fill = "white", stroke =1.2) + 
  xlab("Logit egg-to-adult viability\nchange in selective environment")  + 
  ylab("Logit egg-to-adult viability\nchange in alternative environment")  + 
  ggtitle("Cherry vs. Cranberry") + 
  labs(shape = "Test fruit", color = "Evolution fruit") + 
  scale_shape_manual(values = c(16, 15)) + 
  scale_color_manual(values = c("#BC3C6D", "#FDB424"))  + 
  coord_cartesian(ylim = c(ymin_CheCranG29, ymax_CheCranG29), 
                  xlim = c(ymin_CheCranG29, ymax_CheCranG29)) + 
  theme_LO_sober
 CheCran_G29

# Limits
ymin_CranStrawG29=min(min(TEMP_dataG29_CranStraw_Viability$lowCIlogeggtoadchange_allop, na.rm= TRUE),
                min(TEMP_dataG29_CranStraw_Viability$lowCIlogeggtoadchange_symp, na.rm= TRUE))
ymax_CranStrawG29=max(max(TEMP_dataG29_CranStraw_Viability$upCIlogeggtoadchange_allop, na.rm= TRUE),
                max(TEMP_dataG29_CranStraw_Viability$upCIlogeggtoadchange_symp, na.rm= TRUE))
lim_text<-ymin_CranStrawG29+0.99*(ymax_CranStrawG29-ymin_CranStrawG29)

#Plot
 CranStraw_G29 <- ggplot(data = TEMP_dataG29_CranStraw_Viability) + 
   geom_errorbar(aes(x =logeggtoadchange_symp, ymin = lowCIlogeggtoadchange_allop, 
                    ymax = upCIlogeggtoadchange_allop,
                    color = Fruit_s),
                  width= 0.02, size = 0.3, alpha = 0.8) + 
   geom_errorbarh(aes(y = logeggtoadchange_allop, xmin = lowCIlogeggtoadchange_symp,
                      xmax = upCIlogeggtoadchange_symp, 
                 color = Fruit_s),
                 height = 0.02, size = 0.3, alpha = 0.8) + 
   geom_point(aes(x =logeggtoadchange_symp, y = logeggtoadchange_allop,  
                  color = Fruit_s,fill = Fruit_s, shape = Treatment),
                 size =3, fill = "white", stroke =1.2) + 
  xlab("Logit egg-to-adult viability\nchange in selective environment")  + 
  ylab("Logit egg-to-adult viability\nchange in alternative environment")  + 
   ggtitle("Cranberry vs. Strawberry") + 
   coord_cartesian(ylim = c(ymin_CranStrawG29, ymax_CranStrawG29), 
                  xlim = c(ymin_CranStrawG29, ymax_CranStrawG29)) + 
   labs(shape = "Test fruit", color = "Selection fruit") + 
   scale_shape_manual(values = c(15, 17)) + 
   scale_color_manual(values = c("#FDB424", "#3FAA96"))  + 
   theme_LO_sober
 CranStraw_G29

# Limits
ymin_StrawCheG29=min(min(TEMP_dataG29_StrawChe_Viability$lowCIlogeggtoadchange_allop, na.rm= TRUE),
                min(TEMP_dataG29_StrawChe_Viability$lowCIlogeggtoadchange_symp, na.rm= TRUE))
ymax_StrawCheG29=max(max(TEMP_dataG29_StrawChe_Viability$upCIlogeggtoadchange_allop, na.rm= TRUE),
                max(TEMP_dataG29_StrawChe_Viability$upCIlogeggtoadchange_symp, na.rm= TRUE))
lim_text<-ymin_StrawCheG29+0.99*(ymax_StrawCheG29-ymin_StrawCheG29)


StrawChe_G29 <- ggplot(data = TEMP_dataG29_StrawChe_Viability) + 
  geom_errorbar(aes(x =logeggtoadchange_symp, ymin = lowCIlogeggtoadchange_allop, 
                    ymax = upCIlogeggtoadchange_allop,
                    color = Fruit_s),
                  width= 0.02, size = 0.2, alpha =1) + 
  geom_errorbarh(aes(y = logeggtoadchange_allop, xmin = lowCIlogeggtoadchange_symp, 
                     xmax = upCIlogeggtoadchange_symp, 
                 color = Fruit_s),
                 height = 0.02, size = 0.2, alpha =1) + 
  geom_point(aes(x =logeggtoadchange_symp, y = logeggtoadchange_allop,  
                 color = Fruit_s, fill = Fruit_s, shape = Treatment),
                 size =3, fill = "white", stroke =1.2) + 
  coord_cartesian(ylim = c(ymin_StrawCheG29, ymax_StrawCheG29), 
                  xlim = c(ymin_StrawCheG29, ymax_StrawCheG29)) + 
  xlab("Logit egg-to-adult viability\nchange in selective environment")  + 
  ylab("Logit egg-to-adult viability\nchange in alternative environment")  + 
  ggtitle("Strawberry vs. Cherry") + 
  labs(shape = "Test fruit", color = "Selection fruit") + 
  scale_shape_manual(values = c(16, 17)) + 
  scale_color_manual(values = c("#BC3C6D", "#3FAA96"))  + 
  theme_LO_sober
 StrawChe_G29

legend_tradevide <-  ggplot(data = data_logchange[data_logchange$Generation == "29",],
                          aes(x =logchange, y = logchange,  color = Fruit_s, fill = Fruit_s, 
                              shape = Treatment)) + 
  geom_point(size =2.5, fill = "white") + 
  labs(shape = "Test fruit", color = "Selection fruit") + 
  scale_shape_manual(values = c(16, 15, 17)) + 
  scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96"))  + 
  theme_LO_sober

legend_trade <- lemon::g_legend(legend_tradevide)

 
 

Slopeestimates_logchange_pairwise_errorbar_VIABILITY <- cowplot::ggdraw() + 
  cowplot::draw_plot(CheCran_G7 + theme(legend.position = "none"), 
            x = 0.01, y = 0.5, width = 0.22, height = 0.45) + 
  cowplot::draw_plot(CranStraw_G7 + theme(legend.position = "none"), 
            x = 0.31, y = 0.5, width = 0.22, height = 0.45) + 
  cowplot::draw_plot(StrawChe_G7 + theme(legend.position = "none"), 
            x = 0.61, y = 0.5, width = 0.22, height = 0.45) + 
  cowplot::draw_plot(legend_trade, x = 0.85, y = 0.5, width = 0.1, height = 0.1) + 
  cowplot::draw_plot(CheCran_G29 + theme(legend.position = "none"), 
            x = 0.01, y = 0, width = 0.22, height = 0.45) + 
  cowplot::draw_plot(CranStraw_G29 + theme(legend.position = "none"), 
            x = 0.31, y = 0, width = 0.22, height = 0.45) + 
  cowplot::draw_plot(StrawChe_G29 + theme(legend.position = "none"), 
            x = 0.61, y = 0, width = 0.22, height = 0.45) + 
  cowplot::draw_plot_label(c("Intermediate phenotyping step", "A", "B", "C", " ",
                    "Final phenotyping step", "D", "E", "F", " "),  
                  x = c(0.30, 0.01, 0.30, 0.61, 0.92, 0.250, 0.01, 0.30, 0.61, 0.92), 
                  y = c(0.99, 0.95, 0.95, 0.95, 0.95, 0.49, 0.45, 0.45, 0.45, 0.45), 
                  hjust = c(-0.25, -0.25, -0.25, -0.25, -0.25, -0.75, -0.75, -0.75, -0.75, -0.75), 
                  vjust = c(1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5),
                  size = 16) 
 Slopeestimates_logchange_pairwise_errorbar_VIABILITY

#  
# #  
# 
cowplot::save_plot(file =here::here("figures", "SUPMAT_Correlation_Eggtoadultviability.pdf"),
                   Slopeestimates_logchange_pairwise_errorbar_VIABILITY,
                   base_height = 18/cm(1), base_width = 34/cm(1), dpi = 610)

7 SUP: Pooling effect

7.1 Data

########### Hypothesis 1 
#Hypothesis 1: effect of pool 
#Take the mean of all populations for each selection fruit 
 
CheCran_hypo1 <- computelogchange_POOL_hypo1(data_G7, "Cherry", "Cranberry")



########### Hypothesis 2 
#Hypothesis 2: effect of pool 
#Take the fitness change of the population with the highest fitness in G7 
TEMP_dataG7_CheCran <- formattinglogchange(data_logchange, "7", "Cherry", "Cranberry")
fittest_lines<-computelogchange_POOL_hypo2(data_G7, "Cherry", "Cranberry")
CheCran_hypo2<-TEMP_dataG7_CheCran[TEMP_dataG7_CheCran$Line==fittest_lines,]

7.2 Plot

#Initial plot from main text
ymin = -50
ymax = 50


pd <- position_dodge(0.3) # move them .05 to the left and right

# Limits
ymin_CheCranG29=min(min(TEMP_dataG29_CheCran$logchange_allop-1.96*TEMP_dataG29_CheCran$sd_allop, na.rm= TRUE),
                min(TEMP_dataG29_CheCran$logchange_symp-1.96*TEMP_dataG29_CheCran$sd_symp, na.rm= TRUE))
ymax_CheCranG29=max(max(TEMP_dataG29_CheCran$logchange_allop + 1.96*TEMP_dataG29_CheCran$sd_allop, na.rm= TRUE),
                max(TEMP_dataG29_CheCran$logchange_symp + 1.96*TEMP_dataG29_CheCran$sd_symp, na.rm= TRUE))


CheCran_G29 <- ggplot(data = TEMP_dataG29_CheCran) + 
  geom_errorbar(aes(x =logchange_symp, ymin = logchange_allop-(1.96*sd_allop), 
                    ymax = logchange_allop + (1.96*sd_allop),
                    color = Symp),
                  width= 0.02, size = 0.3, alpha = 0.8) + 
  geom_errorbarh(aes(y = logchange_allop, xmin = logchange_symp-1.96*sd_symp, xmax = logchange_symp + 1.96*sd_symp, 
                 color = Symp),
                 height = 0.02, size = 0.3, alpha = 0.8) + 
  geom_point(aes(x =logchange_symp, y = logchange_allop,  color = Symp,fill = Symp, shape = Allop),
                 size =3, fill = "white", stroke =1.2) + 
  xlab("Fitness change in\nselective environment")  + 
  ylab("Fitness change in\nalternative environment")  + 
  ggtitle("Cherry vs. Cranberry") + 
  labs(shape = "Test fruit", color = "Evolution fruit") + 
  scale_shape_manual(values = c(16, 15)) + 
  scale_color_manual(values = c("#BC3C6D", "#FDB424"))  + 
  coord_cartesian(ylim = c(ymin_CheCranG29, ymax_CheCranG29), 
                  xlim = c(ymin_CheCranG29, ymax_CheCranG29)) + 
  theme_LO_sober
 CheCran_G29
 
 
 
 

PLOT_HYPO_1<-CheCran_G29 +  
  geom_errorbar(data = CheCran_hypo1, aes(x =logchange_symp, ymin = logchange_allop-(1.96*sd_allop), 
                    ymax = logchange_allop + (1.96*sd_allop),
                    color = Symp),
                  width= 0.02, size = 0.2, alpha =1) + 
  geom_errorbarh(data = CheCran_hypo1, 
                 aes(y = logchange_allop, xmin = logchange_symp-1.96*sd_symp, xmax = logchange_symp + 1.96*sd_symp, 
                 color = Symp),
                 height = 0.02, size = 0.2, alpha =1) + 
  geom_point(data = CheCran_hypo1,
             aes(x =logchange_symp, y = logchange_allop,  color = Symp,fill = Symp, shape = Allop),
                 size = 6, fill = "white", stroke = 2) +
    geom_point(data = CheCran_hypo1,
             aes(x =logchange_symp, y = logchange_allop,  fill = Symp, shape = Allop),
                 size = 3, fill = "white", color = "white", stroke = 2) +
  ggtitle("Hypothesis 1: Mean of populations effect")
PLOT_HYPO_1



PLOT_HYPO_2<-CheCran_G29 +  
  geom_errorbar(data = CheCran_hypo2, aes(x = logchange_symp, 
                                          ymin = logchange_allop-(1.96*sd_allop), 
                    ymax = logchange_allop + (1.96*sd_allop),
                    color = Symp),
                  width= 0.02, size = 0.2, alpha =1) + 
  geom_errorbarh(data = CheCran_hypo2, 
                 aes(y = logchange_allop, xmin = logchange_symp-1.96*sd_symp, xmax = logchange_symp + 1.96*sd_symp, 
                 color = Symp),
                 height = 0.02, size = 0.2, alpha =1) + 
  geom_point(data = CheCran_hypo2,
             aes(x =logchange_symp, y = logchange_allop,  color = Symp,fill = Symp, shape = Allop),
                 size = 6, fill = "white", stroke = 2) +
    geom_point(data = CheCran_hypo2,
             aes(x = logchange_symp, y = logchange_allop,  fill = Symp, shape = Allop),
                 size = 3, fill = "white", color = "white", stroke = 2) +
  ggtitle("Hypothesis 2: Fittest population effect")
PLOT_HYPO_2






#legend1
legend_tradevide<- ggplot(CheCran_hypo2, aes(x=logchange_symp, y=logchange_allop, group=Fruit_s, colour=Fruit_s)) +
  geom_point(size=1.5, position=pd, fill="white", alpha=0.6) + 
  geom_errorbar(aes(ymin=logchange_allop-(1.96*sd_allop), ymax=logchange_allop+(1.96*sd_allop),),
                  width=0.4,size=0.4, alpha=0.3) +
  labs(colour = "Population \nevolved on:") + 
  scale_color_manual(values=c("#BC3C6D", "#FDB424","#3FAA96"), 
                     label=c("Cherry","Cranberry", "Strawberry")) +   
  theme_LO_sober +
  theme(legend.title = element_text(colour="black", size=10, face="bold"), 
          legend.text = element_text(colour="black", size=8))

legend_tradevide<-lemon::g_legend(legend_tradevide)

 #legend hypo 1
legend_hypo1<- ggplot(CheCran_hypo2, aes(x=logchange_symp, y=logchange_allop, group=Fruit_s, colour=Fruit_s)) +
  geom_point(size=1.5, position=pd, fill="white", alpha=0.6) + 
  geom_errorbar(aes(ymin=logchange_allop-(1.96*sd_allop), ymax=logchange_allop+(1.96*sd_allop),),
                  width=0.4,size=0.4, alpha=0.3) +
  labs(colour = "Mean of population\nevolved on:") + 
  geom_point(size=2, shape=21, fill="white", stroke = 2) + 
  scale_color_manual(values=c("#BC3C6D", "#FDB424","#3FAA96"), 
                     label=c("Cherry","Cranberry", "Strawberry")) +   
  theme_LO_sober +
  theme(legend.title = element_text(colour="black", size=10, face="bold"), 
          legend.text = element_text(colour="black", size=8))

extract_legend_hypo1<-lemon::g_legend(legend_hypo1)

 #legend2
legend_hypo2<- ggplot(CheCran_hypo2, aes(x=logchange_symp, y=logchange_allop, group=Fruit_s, colour=Fruit_s)) +
  geom_point(size=1.5, position=pd, fill="white", alpha=0.6) + 
  geom_errorbar(aes(ymin=logchange_allop-(1.96*sd_allop), ymax=logchange_allop+(1.96*sd_allop),),
                  width=0.4,size=0.4, alpha=0.3) +
  labs(colour = "Population with \nthe highest fitness \non each selection fruit:") + 
  geom_point(size=2, shape=21, fill="white", stroke = 2) + 
  scale_color_manual(values=c("#BC3C6D", "#FDB424","#3FAA96"), 
                     label=c("Cherry","Cranberry", "Strawberry")) +   
  theme_LO_sober +
  theme(legend.title = element_text(colour="black", size=10, face="bold"), 
          legend.text = element_text(colour="black", size=8))

extract_legend_hypo2<-lemon::g_legend(legend_hypo2)



ERRORCROSSES_G7_HYPO1_2 <- cowplot::ggdraw() +
  cowplot::draw_plot(PLOT_HYPO_1+theme(legend.position = "none"), 
            x =0.00, y = 0.5, width = 0.75, height = 0.5) +
  cowplot::draw_plot(PLOT_HYPO_2+theme(legend.position = "none"), 
            x = 0.0, y = 0, width = 0.75, height = 0.5) +
  cowplot::draw_plot(legend_tradevide, x = 0.78, y = 0.85, width = 0.1, height = 0.1) +
  cowplot::draw_plot(extract_legend_hypo1, x = 0.82, y = 0.7, width = 0.1, height = 0.1) +
  cowplot::draw_plot(legend_tradevide, x = 0.76, y = 0.35, width = 0.1, height = 0.1) +
  cowplot::draw_plot(extract_legend_hypo2, x = 0.82, y = 0.2, width = 0.1, height = 0.1) 
ERRORCROSSES_G7_HYPO1_2





ERRORCROSSES_G7_HYPO1 <- cowplot::ggdraw() +
  cowplot::draw_plot(PLOT_HYPO_1+theme(legend.position = "none", 
                                       plot.title = element_blank()), 
            x =0.00, y = 0, width = 0.75, height = 1) +
  cowplot::draw_plot(legend_tradevide, x = 0.78, y = 0.3, width = 0.1, height = 0.1) +
  cowplot::draw_plot(extract_legend_hypo1, x = 0.82, y = 0.55, width = 0.1, height = 0.1)
ERRORCROSSES_G7_HYPO1

ERRORCROSSES_G7_HYPO2 <- cowplot::ggdraw() +
  cowplot::draw_plot(PLOT_HYPO_2+theme(legend.position = "none", 
                                       plot.title = element_blank()), 
            x =0.00, y = 0, width = 0.75, height = 1) +
  cowplot::draw_plot(legend_tradevide, x = 0.76, y = 0.3, width = 0.1, height = 0.1) +
  cowplot::draw_plot(extract_legend_hypo2, x = 0.82, y = 0.55, width = 0.1, height = 0.1)
ERRORCROSSES_G7_HYPO2




# 
# cowplot::save_plot(file =here::here("figures", "SUPMAT_Pooleffect_Hypothesis.pdf"),
#                    ERRORCROSSES_G7_HYPO1_2,
#                    base_height = 18/cm(1), base_width = 15/cm(1), dpi = 610)
# 
# 

cowplot::save_plot(file =here::here("figures", "SUPMAT_Pooleffect_Hypothesis1.pdf"),
                   ERRORCROSSES_G7_HYPO1,
                   base_height = 12/cm(1), base_width = 15/cm(1), dpi = 610)


cowplot::save_plot(file =here::here("figures", "SUPMAT_Pooleffect_Hypothesis2.pdf"),
                   ERRORCROSSES_G7_HYPO2,
                   base_height = 12/cm(1), base_width = 15/cm(1), dpi = 610)

7.3 A SUPR

PAIR_StrawChe_G29
PAIR_CranStraw_G29
PAIR_CheCran_G29


## Data
# EC_Cran_Cher_G7<-formattingplotpair(data_logchange, "7", "Cherry", "Cranberry")
# EC_Straw_Cran_G7<-formattingplotpair(data_logchange, "7", "Cranberry", "Strawberry")
# EC_Straw_Cher_G7<-formattingplotpair(data_logchange, "7", "Strawberry", "Cherry")

########### Hypothesis 1 
#Hypothesis 1: effect of pool 
#Take the mean of all populations for each selection fruit 
dataset_hypo1 <- formatting_POOL_hypo1(fitness_dataset_intermediate = data_G7)

########### Hypothesis 2 
#Hypothesis 2: effect of pool 
#Take the fitness change of the population with the highest fitness in G7 
fittest_lines<-formatting_POOL_hypo2(data_G7)
EC_Cran_Cher_hypo2<-EC_Cran_Cher_G7[EC_Cran_Cher_G7$Line == fittest_lines[1]|
                                      EC_Cran_Cher_G7$Line == fittest_lines[2]|
                                      EC_Cran_Cher_G7$Line == fittest_lines[3],]
EC_Straw_Cran_hypo2<-EC_Straw_Cran_G7[EC_Straw_Cran_G7$Line == fittest_lines[1]|
                                       EC_Straw_Cran_G7$Line == fittest_lines[2]|
                                        EC_Straw_Cran_G7$Line == fittest_lines[3],]
EC_Straw_Cher_hypo2<-EC_Straw_Cher_G7[EC_Straw_Cher_G7$Line == fittest_lines[1]|
                                       EC_Straw_Cher_G7$Line == fittest_lines[2]|
                                        EC_Straw_Cher_G7$Line == fittest_lines[3],]


PAIR_CheCran_G29_HYPO_1 <- PAIR_CheCran_G29 +
  geom_errorbar(data = dataset_hypo1, 
                aes(x=logchange_Strawberry, 
                    ymin = logchange_Cranberry-1.96*sd_logchange_Cranberry,
                    ymax = logchange_Cranberry+1.96*sd_logchange_Cranberry, Line=NA),
                width=0.02, size=0.2, alpha=1) + 
  geom_errorbarh(data = dataset_hypo1, 
                 aes(y=logchange_Cranberry, 
                     xmin = logchange_Strawberry-1.96*sd_logchange_Strawberry,
                     xmax = logchange_Strawberry+1.96*sd_logchange_Strawberry),
                 height=0.02, size=0.2, alpha=1) + 
  geom_point(data = dataset_hypo1, 
             aes(x = logchange_Strawberry, y = logchange_Cranberry), 
             shape=21, size=4,  fill = "#ffffff", stroke=1.5)
PAIR_CheCran_G29_HYPO_1


PAIR_StrawChe_G29_HYPO_1<-PAIR_StrawChe_G29 +
  geom_errorbar(data = dataset_hypo1, 
                aes(x=logchange_Strawberry, 
                    ymin = logchange_Cranberry-1.96*sd_logchange_Cranberry,
                    ymax = logchange_Cranberry+1.96*sd_logchange_Cranberry, Line=NA),
                width=0.02, size=0.2, alpha=1) + 
  geom_errorbarh(data = dataset_hypo1, 
                 aes(y=logchange_Cranberry, 
                     xmin = logchange_Strawberry-1.96*sd_logchange_Strawberry,
                     xmax = logchange_Strawberry+1.96*sd_logchange_Strawberry),
                 height=0.02, size=0.2, alpha=1) + 
  geom_point(data = dataset_hypo1, 
             aes(x = logchange_Strawberry, y = logchange_Cranberry), 
             shape=21, size=4,  fill = "#ffffff", stroke=1.5)
PAIR_StrawChe_G29_HYPO_1


PAIR_CranStraw_G29_HYPO_1<-PAIR_StrawChe_G29  + 
  geom_errorbar(data = dataset_hypo1, 
                aes(x=logchange_Strawberry, 
                    ymin = logchange_Cranberry-1.96*sd_logchange_Cranberry,
                    ymax = logchange_Cranberry+1.96*sd_logchange_Cranberry, Line=NA),
                width=0.02, size=0.2, alpha=1) + 
  geom_errorbarh(data = dataset_hypo1, 
                 aes(y=logchange_Cranberry, 
                     xmin = logchange_Strawberry-1.96*sd_logchange_Strawberry,
                     xmax = logchange_Strawberry+1.96*sd_logchange_Strawberry),
                 height=0.02, size=0.2, alpha=1) + 
  geom_point(data = dataset_hypo1, 
             aes(x = logchange_Strawberry, y = logchange_Cranberry), 
             shape=21, size=4,  fill = "#ffffff", stroke=1.5)
PAIR_CranStraw_G29_HYPO_1
 












PAIR_CranStraw_G29_HYPO_2 <- PAIR_CranStraw_G29 +
  geom_errorbar(data = EC_Straw_Cran_hypo2, 
                aes(x=logchange_Strawberry, 
                    ymin = logchange_Cranberry-1.96*sd_logchange_Cranberry,
                    ymax = logchange_Cranberry+1.96*sd_logchange_Cranberry),
                width=0.02, size=0.2, alpha=1) + 
  geom_errorbarh(data = EC_Straw_Cran_hypo2, 
                 aes(y=logchange_Cranberry, 
                     xmin = logchange_Strawberry-1.96*sd_logchange_Strawberry,
                     xmax = logchange_Strawberry+1.96*sd_logchange_Strawberry),
                 height=0.02, size=0.2, alpha=1) + 
  geom_point(data = EC_Straw_Cran_hypo2, 
             aes(x = logchange_Strawberry, y = logchange_Cranberry), 
             shape=21, size=4,  fill = "#ffffff", stroke=1.5)
PAIR_CranStraw_G29_HYPO_2



PAIR_StrawChe_G29_HYPO_2 <- PAIR_StrawChe_G29 +
  geom_errorbar(data = EC_Straw_Cher_hypo2, 
                aes(x=logchange_Cherry, 
                    ymin = logchange_Strawberry-1.96*sd_logchange_Strawberry,
                    ymax = logchange_Strawberry+1.96*sd_logchange_Strawberry),
                width=0.02, size=0.2, alpha=1) + 
  geom_errorbarh(data = EC_Straw_Cher_hypo2, 
                 aes(y=logchange_Strawberry, 
                     xmin = logchange_Cherry-1.96*sd_logchange_Cherry,
                     xmax = logchange_Cherry+1.96*sd_logchange_Cherry),
                 height=0.02, size=0.2, alpha=1) + 
  geom_point(data = EC_Straw_Cher_hypo2, 
             aes(x = logchange_Cherry, y = logchange_Strawberry), 
             shape=21, size=4,  fill = "#ffffff", stroke=1.5)
PAIR_StrawChe_G29_HYPO_2


PAIR_CheCran_G29_HYPO_2<-PAIR_CheCran_G29  + 
  geom_errorbar(data = EC_Cran_Cher_hypo2, 
                aes(x=logchange_Cranberry, 
                    ymin = logchange_Cherry-1.96*sd_logchange_Cherry,
                    ymax = logchange_Cherry+1.96*sd_logchange_Cherry),
                width=0.02, size=0.2, alpha=1) + 
  geom_errorbarh(data = EC_Cran_Cher_hypo2, 
                 aes(y=logchange_Cherry, 
                     xmin = logchange_Cranberry-1.96*sd_logchange_Cranberry,
                     xmax = logchange_Cranberry+1.96*sd_logchange_Cranberry),
                 height=0.02, size=0.2, alpha=1) + 
  geom_point(data = EC_Cran_Cher_hypo2, 
             aes(x = logchange_Cranberry, y = logchange_Cherry), 
             shape=21, size=4,  fill = "#ffffff", stroke=1.5)
PAIR_CheCran_G29_HYPO_2



#legend1
legend_tradevide<- ggplot(EC_Cran_Cher_hypo2, aes(x=logchange_Cranberry, 
                                                  y=logchange_Cherry, group=Fruit_s, colour=Fruit_s)) +
  geom_point(size=1.5, position=pd, fill="white", alpha=0.6) + 
  labs(colour = "Population \nevolved on:") + 
  scale_color_manual(values=c("#BC3C6D", "#FDB424","#3FAA96"), 
                     label=c("Cherry","Cranberry", "Strawberry")) +   
  theme_LO_sober +
  theme(legend.title = element_text(colour="black", size=10, face="bold"), 
          legend.text = element_text(colour="black", size=8))

legend_tradevide<-lemon::g_legend(legend_tradevide)

 #legend hypo 1
legend_hypo1<- ggplot(EC_Cran_Cher_hypo2, aes(x=logchange_Cranberry, 
                                                  y=logchange_Cherry, group=Fruit_s, colour=Fruit_s)) +
  geom_point(size=1.5, position=pd, fill="white", alpha=0.6) + 
  labs(colour = "Mean of population\nevolved on:") + 
  geom_point(size=2, shape=21, fill="white", stroke = 2) + 
  scale_color_manual(values=c("#BC3C6D", "#FDB424","#3FAA96"), 
                     label=c("Cherry","Cranberry", "Strawberry")) +   
  theme_LO_sober +
  theme(legend.title = element_text(colour="black", size=10, face="bold"), 
          legend.text = element_text(colour="black", size=8))

extract_legend_hypo1<-lemon::g_legend(legend_hypo1)

 #legend2
legend_hypo2<- ggplot(EC_Cran_Cher_hypo2, aes(x=logchange_Cranberry, 
                                                  y=logchange_Cherry, group=Fruit_s, colour=Fruit_s)) +
  geom_point(size=1.5, position=pd, fill="white", alpha=0.6) + 
  labs(colour = "Population with \nthe highest fitness \non each selection fruit:") + 
  geom_point(size=2, shape=21, fill="white", stroke = 2) + 
  scale_color_manual(values=c("#BC3C6D", "#FDB424","#3FAA96"), 
                     label=c("Cherry","Cranberry", "Strawberry")) +   
  theme_LO_sober +
  theme(legend.title = element_text(colour="black", size=10, face="bold"), 
          legend.text = element_text(colour="black", size=8))
extract_legend_hypo2<-lemon::g_legend(legend_hypo2)




ERRORCROSSES_G7_HYPO1 <- cowplot::ggdraw() +
  cowplot::draw_plot(PAIR_CheCran_G29_HYPO_1+theme(legend.position = "none"), 
            x =0.00, y = 0, width = 0.25, height = 1) +
  cowplot::draw_plot(PAIR_CranStraw_G29_HYPO_1+theme(legend.position = "none"), 
            x = 0.26, y = 0, width = 0.25, height = 1) +
  cowplot::draw_plot(PAIR_StrawChe_G29_HYPO_1+theme(legend.position = "none"), 
            x = 0.52, y = 0, width = 0.25, height = 1) + 
  cowplot::draw_plot(legend_tradevide, x = 0.8, y = 0.73, width = 0.11, height = 0.1) +
  cowplot::draw_plot(extract_legend_hypo1, x = 0.83, y = 0.25, width = 0.1, height = 0.1) 
ERRORCROSSES_G7_HYPO1



ERRORCROSSES_G7_HYPO2 <- cowplot::ggdraw() +
  cowplot::draw_plot(PAIR_CheCran_G29_HYPO_2+theme(legend.position = "none"), 
            x =0.00, y = 0, width = 0.25, height = 1) +
  cowplot::draw_plot(PAIR_CranStraw_G29_HYPO_2+theme(legend.position = "none"), 
            x = 0.26, y = 0, width = 0.25, height = 1) +
  cowplot::draw_plot(PAIR_StrawChe_G29_HYPO_2+theme(legend.position = "none"), 
            x = 0.52, y = 0, width = 0.25, height = 1) + 
  cowplot::draw_plot(legend_tradevide, x = 0.78, y = 0.73, width = 0.1, height = 0.1) +
  cowplot::draw_plot(extract_legend_hypo2, x = 0.82, y = 0.25, width = 0.1, height = 0.1) 
ERRORCROSSES_G7_HYPO2








# 
# cowplot::save_plot(file =here::here("figures", "SUPMAT_Pool_Hypothesis1.pdf"),
#                    ERRORCROSSES_G7_HYPO1,
#                    base_height = 7/cm(1), base_width = 28/cm(1), dpi = 610)
# 
# 
# cowplot::save_plot(file =here::here("figures", "SUPMAT_Pool_Hypothesis2.pdf"),
#                    ERRORCROSSES_G7_HYPO2,
#                    base_height = 7/cm(1), base_width = 28/cm(1), dpi = 610)